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Citation: Oshchepkov, D.; Chadaeva,

I.; Kozhemyakina, R.; Zolotareva, K.;

Khandaev, B.; Sharypova, E.;

Ponomarenko, P.; Bogomolov, A.;

Klimova, N.V.; Shikhevich, S.; et al.

Stress Reactivity, Susceptibility to

Hypertension, and Differential

Expression of Genes in Hypertensive

Compared to Normotensive Patients.

Int. J. Mol. Sci. 2022, 23, 2835.

https://doi.org/10.3390/ijms23052835

Academic Editors: Iveta Bernatova,

Monika Bartekova and Silvia Liskova

Received: 30 December 2021

Accepted: 28 February 2022

Published: 4 March 2022

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International Journal of

Molecular Sciences

Article

Stress Reactivity, Susceptibility to Hypertension, andDifferential Expression of Genes in Hypertensive Compared toNormotensive PatientsDmitry Oshchepkov 1 , Irina Chadaeva 1 , Rimma Kozhemyakina 1, Karina Zolotareva 1, Bato Khandaev 1,Ekaterina Sharypova 1, Petr Ponomarenko 1, Anton Bogomolov 1, Natalya V. Klimova 1, Svetlana Shikhevich 1,Olga Redina 1 , Nataliya G. Kolosova 1 , Maria Nazarenko 2, Nikolay A. Kolchanov 1, Arcady Markel 1

and Mikhail Ponomarenko 1,*

1 Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences,630090 Novosibirsk, Russia; diman@bionet.nsc.ru (D.O.); ichadaeva@bionet.nsc.ru (I.C.);korimma@bionet.nsc.ru (R.K.); ka125699ri@yandex.ru (K.Z.); b.khandaev@g.nsu.ru (B.K.);sharypova@bionet.nsc.ru (E.S.); pon.petr@gmail.com (P.P.); mantis_anton@bionet.nsc.ru (A.B.);klimova@bionet.nsc.ru (N.V.K.); shikhsvt@bionet.nsc.ru (S.S.); oredina@bionet.nsc.ru (O.R.);kolosova@bionet.nsc.ru (N.G.K.); kol@bionet.nsc.ru (N.A.K.); markel@bionet.nsc.ru (A.M.)

2 Institute of Medical Genetics, Tomsk National Research Medical Center, 634009 Tomsk, Russia;maria-nazarenko@medgenetics.ru

* Correspondence: pon@bionet.nsc.ru; Tel.: +7-(383)-363-4963 (ext. 1311)

Abstract: Although half of hypertensive patients have hypertensive parents, known hypertension-related human loci identified by genome-wide analysis explain only 3% of hypertension heredity.Therefore, mainstream transcriptome profiling of hypertensive subjects addresses differentiallyexpressed genes (DEGs) specific to gender, age, and comorbidities in accordance with predictivepreventive personalized participatory medicine treating patients according to their symptoms, indi-vidual lifestyle, and genetic background. Within this mainstream paradigm, here, we determinedwhether, among the known hypertension-related DEGs that we could find, there is any genome-widehypertension theranostic molecular marker applicable to everyone, everywhere, anytime. Therefore,we sequenced the hippocampal transcriptome of tame and aggressive rats, corresponding to low andhigh stress reactivity, an increase of which raises hypertensive risk; we identified stress-reactivity-related rat DEGs and compared them with their known homologous hypertension-related animalDEGs. This yielded significant correlations between stress reactivity-related and hypertension-relatedfold changes (log2 values) of these DEG homologs. We found principal components, PC1 and PC2,corresponding to a half-difference and half-sum of these log2 values. Using the DEGs of hypertensiveversus normotensive patients (as the control), we verified the correlations and principal components.This analysis highlighted downregulation of β-protocadherins and hemoglobin as whole-genomehypertension theranostic molecular markers associated with a wide vascular inner diameter and lowblood viscosity, respectively.

Keywords: human; hypertension; stress reactivity; molecular marker; Rattus norvegicus; RNA-Seq;qPCR; differentially expressed gene; meta-analysis; correlation; principal component; bootstrap

1. Introduction

Hypertension is a fatal yet preventable risk factor of ischemic heart disease [1], thetop cause of death worldwide [2]. Besides essential hypertension, there are many casesof hypertension that are not clinically classified as essential. In all these cases, there isan increase in intravascular pressure (only local sometimes) together with vascular shearstress, oxidative stress, inflammatory reactions, and remodeling of the vascular wall. Thesepathogenic mechanisms common to all hypertensive conditions share, at least in part, amolecular basis that we are trying to pinpoint here.

Int. J. Mol. Sci. 2022, 23, 2835. https://doi.org/10.3390/ijms23052835 https://www.mdpi.com/journal/ijms

Int. J. Mol. Sci. 2022, 23, 2835 2 of 28

Although the hypertensive risk increases with age [3,4], the age of the first clinicalmanifestations of hypertension is now diminishing [5]. Preeclampsia (hypertension inpregnancy) is becoming a challenge to obstetricians [6]. Prenatal stress can epigeneticallyreprogram a newborn’s development and can lead to clinical hypertension in adulthood [7].Pulmonary hypertension may start to develop in newborns [8]. Hypertension co-occurswith cancer [9–13], cirrhosis [14], and prostatitis [15]. Hypertension worsens both in-jury [16] and transplantation [17] of a kidney. Epilepsy [18], psoriasis, and dermatitis [19]are associated with hypertension. Anti–SARS-CoV-2 antibody titers are lower in hyper-tensive than in normotensive patients [20]. The “mosaic theory” of hypertension [21] wasrecently enriched with hypertensive development via exosome-dependent inflammationand angiogenesis impairment, associated with endothelial dysfunction and vascular re-modeling [22]. A clinical review [23] revealed that the hypertensive risk increases withan increase in patients’ stress reactivity [24]. Parents of half of the hypertensive patientshad hypertension, but all the known (>60) hypertension-related whole-genome human lociexplained only 3% of this heritability of hypertension [25]. Maybe this is why mainstreamtranscriptome-profiling studies on hypertensive versus normotensive patients [26–39] andanimals [7,40–57] are focused on the differentially expressed genes (DEGs) that are specificto gender, age, and the stage of hypertension development. This is needed in predictivepreventive personalized participatory (4P) medicine [58] to estimate where, how, why,and when hypertension might occur in a given patient depending on his/her geneticbackground. Because hypertension seems to have a finger in every pie, a meta-analysisof all the available specific hypertension-related DEGs can find among them a theranosticmolecular marker of hypertension applicable to everyone, everywhere, anytime.

In our previous studies within this mainstream paradigm, we measured stress reactiv-ity in rats [59] and created an inbred ISIAH rat strain (i.e., inherited stress-induced arterialhypertension) [60] and two outbred strains—tame and aggressive rats—correspondingto low and high stress reactivity [61–64]. On this basis, we sequenced transcriptomes inthe brain stem [43], hypothalamus [44], renal medulla [45], renal cortex [46], and adrenalglands [47] of hypertensive ISIAH rats versus normotensive WAG rats. Besides this, we pro-filed transcriptomes of the hippocampus [40], prefrontal cortex [41], and retina [42] in OXYSrats (ICG SB RAS, Novosibirsk, Russia), which spontaneously develop the accelerated-senescence phenotype against a background of moderately high blood pressure [65–68]with respect to normotensive Wistar rats. In the present work, we meta-analyzed our eightabovementioned RNA-Seq datasets to ensure out of caution that among them (togetherwith those available in PubMed [69]), there are still no invariant molecular markers ofhypertension. Accordingly, we sequenced the hippocampal transcriptome of tame com-pared to aggressive rats and identified the stress-reactivity-related rat DEGs and—usingour bioinformatics model [70–72]—compared them by homology with all the availablehypertension-related animal DEGs that we could find. The results were verified using theDEGs of hypertensive versus normotensive patients.

2. Results2.1. RNA-Seq and Mapping to the Reference Rat Genome

We sequenced the hippocampal transcriptome of three adult male tame gray rats(Rattus norvegicus)—in comparison with that of three aggressive ones—on an IlluminaNextSeq 550 system (see Section 4.2). We chose the hippocampus because its functionscontribute to learning under stress [73]. The rats were derived from two outbred tameand aggressive strains selectively bred at the ICG SB RAS [59,64] for over 90 generationsusing the glove test as described elsewhere [74]. The rats were not consanguineous (seeSection 4.1). This procedure yielded 169,529,658 raw reads of 75 nt in length (Table 1); wedeposited them in the NCBI SRA database [75] (ID PRJNA668014).

Int. J. Mol. Sci. 2022, 23, 2835 3 of 28

Table 1. Summary of searches for differentially expressed genes (DEGs) in hippocampal transcrip-tomes of three tame adult male rats (Rattus norvegicus) and three aggressive ones (all unrelated) inthis work.

Group Tame vs. Aggressive Rats

Total number of sequence reads (NCBI SRA ID:PRJNA668014) 169,529,658

Reads mapped to reference rat genome RGSCRnor_6.0, UCSC Rn6, July 2014 (%) 146,521,467 (88.74%)

Expressed genes identified 14,039Statistically significant DEGs (PADJ < 0.05, Fisher’s

Z-test with Benjamini correction) 42

In Table 1, the reader can see that 146,521,467 reads could be aligned with rat referencegenome Rn6 and yielded 14,039 genes expressed within the hippocampus of the rats understudy. Using Fisher’s Z-test with Benjamini’s correction for multiple comparisons, wefound 42 DEGs that were not hypothetical, tentative, predicted, uncharacterized, or protein-non-coding genes; this approach reduced the false-positive error rates (Tables 1 and 2).

2.2. Quantitative PCR (qPCR)-Based Selective Verification of the DEGs Identified in this Work inthe Hippocampus of Tame versus Aggressive Rats

First, we used 16 additional unrelated rats, namely: eight aggressive and eight tamerats that scored “–3” and “3”, respectively, on a scale from –4 (most aggressive rat) to4 (tamest rat) in the glove test [74] conducted one month before the extraction of hippocam-pus samples (Table 3). Next, among the 42 DEGs listed in Table 2, we chose Ascl3 andDefb17; our qPCR data on them in the hippocampus of the tame and aggressive rats (seeSection 4.4) are in Table 3 as the “mean ± standard error of the mean” (M0 ± SEM) of theirexpression relative to four reference genes (B2m, Hprt1, Ppia, and Rpl30) [76] in triplicate.Arithmetic-mean estimates of the expression levels of each gene (Ascl3 and Defb17) inthe hippocampus of these tame and aggressive rats in question are given in Table 3 andFigure 1a.

According to both the Mann–Whitney U test and Fisher’s Z-test, both Ascl3 andDefb17 are significantly overexpressed in the hippocampus of the tame (white bars) versusaggressive (grey bars) rats according to the qPCR data obtained here (Figure 1a: p < 0.05,asterisks), consistently with the RNA-Seq data (Table 2). Figure 1b depicts a significantPearson’s linear correlation (p < 0.00005), Spearman’s rank correlation (p < 0.05), andKendall’s rank correlation (p < 0.05) between the log2 values (hereinafter, log2: the log2-transformed ratio of an expression level of a given gene in tame rats to that in aggressiverats) for five genes—Ascl3, Defb17, B2m, Ppia, and Rpl30 (open circles)—within the RNA-Seq(X-axis) and qPCR (Y-axis) data obtained here.

Int. J. Mol. Sci. 2022, 23, 2835 4 of 28

Table 2. The statistically significant DEGs in the hippocampus (of tame versus aggressive adult malerats) that were for the first time unidentified in this study.

# Rat Gene, Name Symbol log2 p PADJ

1 Albumin Alb 3.21 <10−11 <10−7

2 Aquaporin 1 (Colton blood group) Aqp1 5.91 <10−6 <10−2

3 Achaete-scute family bHLH transcription factor 3 Ascl3 2.38 <10−4 <0.05

4 BAG cochaperone 3 (synonym: BCL2-associatedathanogene 3) Bag3 −0.92 <10−4 <0.05

5 BAR/IMD domain-containing adaptor protein 2-like 1 Baiap2l1 3.67 <10−4 <0.056 3-hydroxybutyrate dehydrogenase 1 Bdh1 0.40 <10−4 <0.057 Cholecystokinin B receptor Cckbr 1.24 <10−8 <10−4

8 Chondroitin sulfate proteoglycan 4B Cspg4b 3.47 <10−4 <0.059 Defensin β17 Defb17 5.94 <10−4 <0.05

10 Ectonucleotide pyrophosphatase/phosphodiesterase 2 Enpp2 2.41 <10−3 <0.0511 Fras1-related extracellular matrix 1 Frem1 3.16 <10−3 <0.0512 Glycerol-3-phosphate dehydrogenase 1 Gpd1 −1.34 <10−6 <10−3

13 Hemoglobin, β adult major chain Hbb-b1 −6.19 <10−7 <10−4

14 Hepatocyte nuclear factor 4α Hnf4a 6.51 <10−3 <0.05

15 5-hydroxytryptamine receptor 2C (synonym: serotoninreceptor 2C) Htr2c 2.03 <10−3 <0.05

16 Keratin 2 Krt2 −1.43 <10−6 <10−3

17 Leukocyte immunoglobulin-like receptor, subfamily B,member 3-like Lilrb3l 7.45 <10−4 <0.05

18 Lymphocyte antigen 6 complex/Plaur domain-containing 1 Lypd1 −0.89 <10−4 <0.0519 MORN repeat-containing 1 Morn1 1.42 <10−11 <10−7

20 Myomesin 2 Myom2 −1.24 <10−4 <0.0521 Protocadherin β9 Pcdhb9 −1.03 <10−4 <0.0522 Protocadherin γ subfamily A1 Pcdhga1 2.45 <10−4 <0.0523 Prodynorphin Pdyn −0.89 <10−4 <0.0524 Phospholipase A2, group IID Pla2g2d 2.84 <10−4 <0.0525 Phospholipase A2, group V Pla2g5 3.85 <10−4 <0.0526 Procollagen-lysine, 2-oxoglutarate 5-dioxygenase 1 Plod1 −0.67 <10−3 <0.0527 Protein phosphatase 1, regulatory subunit 3B Ppp1r3b 2.45 <10−4 <0.0528 Prolactin receptor Prlr 6.43 <10−5 <10−2

29 Glycogen phosphorylase L Pygl −1.21 <10−5 <0.0530 RNA-binding motif protein 3 Rbm3 0.89 <10−4 <0.0531 Retinol saturase Retsat −0.98 <10−4 <0.0532 Solute carrier family 16, member 12 Slc16a12 3.08 <10−3 <0.0533 Solute carrier family 4, member 5 Slc4a5 6.27 <10−6 <10−3

34 SPARC-related modular calcium-binding 2 Smoc2 −2.09 <10−4 <0.0535 Serine peptidase inhibitor, Kunitz type 1 Spint1 −1.39 <10−7 <10−4

36 Sulfatase 1 Sulf1 3.72 <10−6 <10−2

37 Syncoilin, intermediate filament protein Sync 1.17 <10−3 <0.0538 Tandem C2 domains, nuclear Tc2n 3.47 <10−5 <10−2

39 Tectorin α Tecta 1.38 <10−8 <10−5

40 Transmembrane protein 60 Tmem60 0.79 <10−4 <0.0541 Thioredoxin reductase 2 Txnrd2 −0.71 <10−5 <10−2

42 Uncoupling protein 2 Ucp2 0.73 <10−4 <0.05

Note. Hereinafter, log2: the log2-transformed fold change (i.e., ratio of an expression level of a given gene in tamerats to that in aggressive rats); p and PADJ: statistical significance according to Fisher’s Z-test without and with theBenjamini correction for multiple comparisons, respectively.

Int. J. Mol. Sci. 2022, 23, 2835 5 of 28

Table 3. qPCR data on the selected DEGs from the hippocampus of the independently obtained eighttame adult male rats and eight other aggressive ones (all unrelated animals).

Design Behavioral “Glove” Test [74] and the qPCR Data on Gene Expression [This Work]

Rat Set No. 1 2 3 4 5 6 7 8

GlovetestA −3 −3 −3 −3 −3 −3 −3 −3T 3 3 3 3 3 3 3 3

DEG Set Relative expression with respect to four reference genes, qPCR, M0 ± SEM TOTAL

Ascl3A 0.16 ±

0.020.88 ±

0.300.82 ±

0.080.09 ±

0.040.18 ±

0.030.07 ±

0.070.27 ±

0.110.32 ±

0.050.35 ±

0.17

T 4.85 ±4.38

3.40 ±1.69

1.75 ±0.24

2.21 ±0.12

2.92 ±0.05

4.48 ±0.17

3.83 ±0.33

2.64 ±0.15

3.26 ±1.71

Defb17 A 0.005 ±0.005

0.01 ±0.005

0.005 ±0.005

0.005 ±0.005

0.005 ±0.005 ND 0.005 ±

0.0050.005 ±

0.0050.01 ±

0.01

T 1.72 ±0.04

3.22 ±0.42

2.52 ±0.14

1.82 ±0.55

2.45 ±0.10

4.43 ±0.26

1.99 ±0.89

2.34 ±0.27

2.56 ±0.53

Note. Sets: A, aggressive rats; T, tame rats; qPCR data: “M0 ± SEM” denotes the mean ± standard error of themean for three technical replicates for each rat; ND, not detected.

Int. J. Mol. Sci. 2022, 23, x FOR PEER REVIEW 5 of 28

Figure 1. qPCR-based selective verification of the DEGs identified by RNA-Seq in this work in the hippocampus of tame versus aggressive rats. Legend: (a) in tame male adult rats (white bars) versus aggressive ones (grey bars), both DEGs examined (i.e., Ascl3 and Defb17) are statistically signifi-cantly overexpressed in the hippocampus (here, bar height (i.e., mean), error bars (i.e., standard error of the mean [SEM]), and asterisks denote statistical significance at p < 0.05 according to both the nonparametric Mann–Whitney U-test and parametric Fisher’s Z-test). Asterisk (symbol “*”), sta-tistically significant at p < 0.05. (b) Statistically significant correlations between the relative expres-sion levels of the two selected DEGs and three reference genes (i.e., B2m (β-2-microglobulin), Ppia (peptidylprolyl isomerase A), and Rpl30 (ribosomal protein L30)) in the hippocampus of tame ver-sus aggressive rats (open circles), as measured experimentally by RNA-Seq (X-axis) and qPCR (Y-axis) and presented on the log2 scale (see “Materials and Methods”). Dashed and dash-and-dot lines denote linear regression and boundaries of its 95% confidence interval calculated using Statistica software (StatsoftTM, Tulsa, OK, USA). r, R, τ, and p are coefficients of Pearson’s linear correlation, Spearman’s rank correlation, Kendall’s rank correlation, and their p values (statistical significance), respectively.

According to both the Mann–Whitney U test and Fisher’s Z-test, both Ascl3 and Defb17 are significantly overexpressed in the hippocampus of the tame (white bars) versus aggressive (grey bars) rats according to the qPCR data obtained here (Figure 1a: p < 0.05, asterisks), consistently with the RNA-Seq data (Table 2). Figure 1b depicts a significant Pearson’s linear correlation (p < 0.00005), Spearman’s rank correlation (p < 0.05), and Ken-dall’s rank correlation (p < 0.05) between the log2 values (hereinafter, log2: the log2-trans-formed ratio of an expression level of a given gene in tame rats to that in aggressive rats) for five genes—Ascl3, Defb17, B2m, Ppia, and Rpl30 (open circles)—within the RNA-Seq (X-axis) and qPCR (Y-axis) data obtained here. 2.3. Comparison of the Known DEGs (of Hypertensive versus Normotensive Animals) with Their Homologous Genes among the 42 Hippocampal DEGs (of Tame versus Aggressive Rats) Identified Here

In this study, using the PubMed database [69], we compiled all the transcriptomes (that we could find) of hypertensive versus normotensive animals, as presented in Table 4. The total number of DEGs was 4216 in 14 tissues of four animal species, as cited in the rightmost column of Table 4 [7,40–57].

Figure 1. qPCR-based selective verification of the DEGs identified by RNA-Seq in this work inthe hippocampus of tame versus aggressive rats. Legend: (a) in tame male adult rats (white bars)versus aggressive ones (grey bars), both DEGs examined (i.e., Ascl3 and Defb17) are statisticallysignificantly overexpressed in the hippocampus (here, bar height (i.e., mean), error bars (i.e., standarderror of the mean [SEM]), and asterisks denote statistical significance at p < 0.05 according to boththe nonparametric Mann–Whitney U-test and parametric Fisher’s Z-test). Asterisk (symbol “*”),statistically significant at p < 0.05. (b) Statistically significant correlations between the relativeexpression levels of the two selected DEGs and three reference genes (i.e., B2m (β-2-microglobulin),Ppia (peptidylprolyl isomerase A), and Rpl30 (ribosomal protein L30)) in the hippocampus of tameversus aggressive rats (open circles), as measured experimentally by RNA-Seq (X-axis) and qPCR(Y-axis) and presented on the log2 scale (see “Materials and Methods”). Dashed and dash-and-dotlines denote linear regression and boundaries of its 95% confidence interval calculated using Statisticasoftware (StatsoftTM, Tulsa, OK, USA). r, R, τ, and p are coefficients of Pearson’s linear correlation,Spearman’s rank correlation, Kendall’s rank correlation, and their p values (statistical significance),respectively.

Int. J. Mol. Sci. 2022, 23, 2835 6 of 28

2.3. Comparison of the Known DEGs (of Hypertensive versus Normotensive Animals) with TheirHomologous Genes among the 42 Hippocampal DEGs (of Tame versus Aggressive Rats) IdentifiedHere

In this study, using the PubMed database [69], we compiled all the transcriptomes(that we could find) of hypertensive versus normotensive animals, as presented in Table 4.The total number of DEGs was 4216 in 14 tissues of four animal species, as cited in therightmost column of Table 4 [7,40–57].

Table 4. The DEGs—of hypertensive versus normotensive animals—that we could find (available inPubMed [69]).

# Species Hypertensive Normotensive Tissue NDEG Ref.

1 rat OXYS Wistar hippocampus 85 [40]

2 rat OXYS Wistar prefrontalcortex 73 [41]

3 rat OXYS Wistar retina 85 [42]4 rat ISIAH WAG brain stem 206 [43]5 rat ISIAH WAG hypothalamus 137 [44]6 rat ISIAH WAG renal medulla 882 [45]7 rat ISIAH WAG renal cortex 309 [46]8 rat ISIAH WAG adrenal gland 1020 [47]9 rat SHR Wistar brain pericytes 21 [48]10 rat SHR Wistar kidney 35 [49]11 rat SD, monocrotaline-treated SD, saline-treated lung 10 [50]12 rat Dahl-SS, water after salt diet Dahl-SS, QSYQ after salt diet kidney 13 [51]13 rat Resp18-null Dahl-SS Dahl-SS kidney 14 [52]14 rat prenatal dexamethasone stress norm adrenal gland 93 [7]

15 mice Toxoplasma infection inpregnancy norm uterus 10 [53]

16 mice BPH/2J BPN/3J kidney 883 [54]

17 rabbit G2K1C-treated norm middle cerebralartery 230 [55]

18 chicken high (1.2%) Ca diet normal (0.8%) Ca diet kidney 92 [56]

19 chicken cold stress with salt diet healthy chicken pulmonaryarteries 18 [57]

Σ 4 species 14 animal models of human hypertension 14 tissues 4216

Note. NDEG: the number of DEGs; BPH/2J, BPN/3J Dahl-SS, ISIAH, OXIS, SD, SHR, WAG, and Wistar: laboratoryanimal strains; QSYQ: QiShenYiQi pills, a cardioprotective remedy from traditional Chinese medicine; G2K1C:Goldblatt 2-kidney 1-clip; Ref.: reference.

Figure S1 (hereinafter: see Supplementary Materials) depicts how we compared 4216DEGs of hypertensive versus normotensive animals (Table 4) with 42 hippocampal DEGs ofthe tame versus aggressive rats (Table 2). First, we compiled 151 pairs of homologous DEGs,where one DEG was taken from Table 2, while its homologous DEG was found amongthe 4216 DEGs described in Table 4 (both are in Table S1 (hereinafter: see SupplementaryMaterials)), as shown in Figure S1 using a Venn diagram and in the table. Next, for the firsttime. we found that stress-reactivity-related and hypertension-related log2 values of thehomologous animal DEGs statistically significantly correlate with each other accordingto Pearson’s linear correlation (r = −0.29, p < 0.0005), the Goodman–Kruskal generalizedcorrelation (γ = −0.20, p < 0.0005), and Spearman’s (R = −0.29, p < 0.00025) and Kendall’s(τ = −0.20, p < 0.0005) rank correlations. Finally, we processed Table S1 by principalcomponent analysis in the Bootstrap mode of the PAST4.04 software [77] that yieldedprincipal components PC1 and PC2, corresponding to a half-difference and half-sum of thestress reactivity-related and hypertension-related log2 values of the homologous animalDEGs (Figure S1).

Int. J. Mol. Sci. 2022, 23, 2835 7 of 28

2.4. Verification of the Results Obtained on the Hypertensive versus Normotensive AnimalsExamined in this Work with respect to the DEGs—Of Hypertensive versus NormotensivePatients—That We Could Find

Using the PubMed database [69], we collected all the DEGs (of hypertensive comparedwith normotensive patients) that we could find (Table 5). The total number of hypertension-related human DEGs found was 7865, as cited in the rightmost column of Table 5 [26–39].

Table 5. The analyzed DEGs—of hypertensive versus normotensive patients—that we could find(available in PubMed [69]).

# Hypertensive Normotensive Tissue NDEG Ref.

1 renal medullary hypertension norm renal medulla 13 [26]2 pulmonary arterial hypertension norm lung 49 [27]3 pulmonary arterial hypertension norm lung 119 [28]

4 men with pulmonary arterialhypertension normal men blood 14 [29]

5 women with pulmonary arterialhypertension normal women blood 15 [29]

6 pulmonary hypertension duringpulmonary fibrosis norm lung 3520 [30]

7 BMPR2-deficient human cells normal cells pulmonary arteryendothelial cells 483 [31]

8 preeclampsia normal pregnant placenta 1228 [32]9 preeclampsia normal pregnant placenta 10 [33]

10 preeclampsia normal pregnant venous blood 64 [34]11 preeclampsia normal pregnant decidua basalis 372 [35]

12 excessive miR-210 in SWAN-71 cells normal SWAN-71cells

trophoblast cell lineSWAN-71 19 [36]

13 hypertension-induced nephrosclerosis norm kidney 16 [37]

14 hypertension-related pre-invasivesquamous cancer

normal cells, thesame biopsies

squamous lung cancercells 119 [38]

15 hypertension-induced atrial fibrillation norm auricle tissue biopsy 300 [39]

16 hypertension-induced coronary arterydisease norm peripheral blood 1524 [39]

Σ 10 human hypertension-related disorders 12 tissues 7865

Note. See the footnote of Table 4.

Figure 2 shows exactly how we reproduced step-by-step the results obtained from thehypertension-related animal DEGs only by replacing them with the hypertension-relatedhuman DEGs (Table 5) as independent control clinical data that are documented in TableS2. The lower half of this figure presents robust correlations between the stress-reactivity-related and hypertension-related log2 values corresponding to animal and human DEGhomologs as well as principal components PC1 and PC2 proportional to the half-differenceand half-sum, respectively, of these log2 values; this was the essence of the verification.

2.5. Searching for the Hypertension-Related Molecular Markers among the Human GenesOrthologous to the 42 Hippocampal DEGs (of Tame versus Aggressive Rats) Identified in this Work

To this end, first of all, using the PubMed database [69], we characterized each of the42 hippocampal DEGs (of tame versus aggressive rats) identified in this work (Table 2), interms of how downregulation or upregulation of their orthologous human genes can mani-fest itself in hypertension, as presented [78–186] in Table S3 (hereinafter: see SupplementaryMaterials).

Int. J. Mol. Sci. 2022, 23, 2835 8 of 28Int. J. Mol. Sci. 2022, 23, x FOR PEER REVIEW 8 of 28

Figure 2. A step-by-step diagram of verification of the obtained results on the hypertensive versus normotensive animals examined in this work with respect to all the transcriptomes (that we could find) of hypertensive versus normotensive patients (Table 5). Legend: see the footnote of Table 2; PC1 and PC2: principal components calculated using the PAST4.04 software [77].

2.5. Searching for the Hypertension-Related Molecular Markers among the Human Genes Orthologous to the 42 Hippocampal DEGs (of Tame versus Aggressive Rats) Identified in this Work

To this end, first of all, using the PubMed database [69], we characterized each of the 42 hippocampal DEGs (of tame versus aggressive rats) identified in this work (Table 2), in terms of how downregulation or upregulation of their orthologous human genes can manifest itself in hypertension, as presented [78–186] in Table S3 (hereinafter: see Supple-mentary Materials).

Next, for each hippocampal DEG (of tame versus aggressive rats) in question (Table 2), we determined how many homologous DEGs of hypertensive versus normotensive subjects (i.e., patients and animals) have the opposite (NPC1) or the same (NPC2) sign of their log2 values related to hypertension, as compared with the sign of the log2 value of this

Figure 2. A step-by-step diagram of verification of the obtained results on the hypertensive versusnormotensive animals examined in this work with respect to all the transcriptomes (that we couldfind) of hypertensive versus normotensive patients (Table 5). Legend: see the footnote of Table 2; PC1and PC2: principal components calculated using the PAST4.04 software [77].

Next, for each hippocampal DEG (of tame versus aggressive rats) in question (Table 2),we determined how many homologous DEGs of hypertensive versus normotensive subjects(i.e., patients and animals) have the opposite (NPC1) or the same (NPC2) sign of theirlog2 values related to hypertension, as compared with the sign of the log2 value of thishippocampal DEG in tame versus aggressive rats, because principal components PC1and PC2 correspond to a half-difference and half-sum, respectively, of these log2 values(Figure S1 and Figure 2).

Table 6 presents these determined quantities (NPC1 and NPC2) together with theirstatistical significance assessed via the binomial distribution both without (p-values) andwith (PADJ-values) Bonferroni’s correction for multiple comparisons.

Int. J. Mol. Sci. 2022, 23, 2835 9 of 28

Table 6. Searching for hypertension-related molecular markers among the human genes orthologousto the 42 hippocampal DEGs (of tame versus aggressive rats) identified in this work. Here, wetook into account the number of their homologous DEGs in the tissues of hypertensive versusnormotensive subjects (patients and animals).

Rat Gene Total Number of DEGs BinomialDistribution Rat Gene Total Number of DEGs Binomial

Distribution

# SymbolNPC1:

OppositeSigns

NPC2:Matching

Signsp PADJ # Symbol

NPC1:Opposite

Signs

NPC2:Matching

Signsp PADJ

i ii iii iv v vi i ii iii iv v vi

1 Alb 1 1 0.75 1.00 22 Pcdhga1 1 1 0.75 1.002 Aqp1 6 6 0.61 1.00 23 Pdyn 0 0 ND ND3 Ascl3 1 1 0.75 1.00 24 Pla2g2d 19 12 0.14 1.004 Bag3 2 2 0.69 1.00 25 Pla2g5 19 12 0.14 1.005 Baiap2l1 1 0 0.50 1.00 26 Plod1 3 1 0.31 1.006 Bdh1 2 0 0.25 1.00 27 Ppp1r3b 2 3 0.50 1.007 Cckbr 1 0 0.50 1.00 28 Prlr 0 1 0.50 1.008 Cspg4b 0 0 ND ND 29 Pygl 0 1 0.50 1.009 Defb17 2 3 0.50 1.00 30 Rbm3 15 12 0.35 1.00

10 Enpp2 3 8 0.11 1.00 31 Retsat 5 2 0.23 1.0011 Frem1 1 1 0.75 1.00 32 Slc16a12 7 6 0.50 1.0012 Gpd1 4 1 0.19 1.00 33 Slc4a5 7 5 0.83 1.0013 Hbb-b1 24 3 10−4 10−3 34 Smoc2 3 1 0.31 1.0014 Hnf4a 0 0 ND ND 35 Spint1 1 1 0.75 1.0015 Htr2c 3 3 0.65 1.00 36 Sulf1 0 0 ND ND16 Krt2 22 13 0.09 1.00 37 Sync 0 0 ND ND17 Lilrb3l 10 1 10−2 0.24 38 Tc2n 2 0 0.25 1.0018 Lypd1 11 7 0.24 1.00 39 Tecta 0 1 0.50 1.0019 Morn1 0 4 0.06 1.00 40 Tmem60 0 0 ND ND20 Myom2 2 1 0.50 1.00 41 Txnrd2 2 0 0.25 1.0021 Pcdhb9 10 0 10−3 0.05 42 Ucp2 1 1 0.75 1.00

Note. p and PADJ: a significance estimate according to the binomial distribution without or with Bonfer-roni’s correction for multiple comparisons, respectively; ND: not detected; underlining: statistically significanthypertension-related molecular markers identified in this work.

As shown in this table, only two of the 42 DEGs (in the hippocampus of the tame versusaggressive rats) found here are linked with PC1 (i.e., Hbb-b1 and Pcdhb9, as described inTable 7). Looking through Table 7, readers can see the statistically significant upregulationof both β-protocadherin and hemoglobin subunit DEGs in the tissues of the hypertensiveversus normotensive subjects (patients and animals). This result allowed us to propose thestatistically significant downregulation of their homologous DEGs (in the hippocampus oftame versus aggressive rats), identified here (Table 2) as candidate hypertension theranosticmolecular markers.

2.6. Verification of Downregulation of Human β-Hemoglobin and β-Protocadherins asHypertensionTtheranostic Molecular Markers using the DEGs (That We Could Find) of Domesticversus Wild Animals

For this purpose, using the PubMed database [69], we collected all the transcriptomes(that we could find) of domestic animals compared with their wild congeners, as shown inTable 8. The bottom row of this table indicates that we found 2393 DEGs in the tissues ofdomestic versus wild animals, as cited in the rightmost column of this table [72,187–193].

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Table 7. Statistically significant upregulation of the hemoglobin subunit and β-protocadherin DEGs—in the tissues of the hypertensive versus normotensive subjects (i.e., patients and animals)—that werefor the first time compiled together here.

# Species Hypertensive Normotensive Tissue DEG log2 PADJ Ref.

i ii iii iv v v vi vii viii

1 rat ISIAH WAG brain stem Hbb-b1 1.42 10−2 [43]2 rat ISIAH WAG hypothalamus Hbb-b1 2.02 10−2 [44]3 rat ISIAH WAG renal medulla Hbb-b1 1.18 10−2 [45]4 rat ISIAH WAG adrenal gland Hbb-b1 1.32 10−2 [47]5 rat ISIAH WAG adrenal gland Hba2 0.69 10−2 [47]6 rat ISIAH WAG adrenal gland Hbb 2.02 10−2 [47]7 rat ISIAH WAG adrenal gland Hbb-m 3.78 10−2 [47]8 rat ISIAH WAG brain stem Hba2 0.58 0.05 [43]9 rat ISIAH WAG brain stem Hbb 1.88 10−2 [43]

10 rat ISIAH WAG brain stem Hbb-m 3.65 10−2 [43]11 rat ISIAH WAG hypothalamus Hba1 1.14 10−2 [44]12 rat ISIAH WAG hypothalamus Hba2 1.32 10−2 [44]13 rat ISIAH WAG hypothalamus Hbb 3.23 10−2 [44]14 rat ISIAH WAG hypothalamus Hbb-m 1.09 10−2 [44]15 rat ISIAH WAG renal medulla Hbb −0.68 10−2 [45]16 rat ISIAH WAG renal medulla Hbb-m 2.72 10−2 [45]17 rat ISIAH WAG renal medulla Hbb-s 2.38 10−2 [45]18 human preeclampsia norm placenta HBD −0.63 10−3 [32]

19 human pulmonary hypertension duringpulmonary fibrosis norm lungs HBD −2.83 10−3 [30]

20 human pulmonary hypertension norm lungs HBA1 2.08 10−9 [28]21 human pulmonary hypertension norm lungs HBB 2.46 10−10 [28]22 human HT-induced coronary disease norm peripheral blood HBBP1 1.03 0.05 [39]23 human HT-induced coronary disease norm peripheral blood HBE1 1.42 0.05 [39]24 human HT-induced coronary disease norm peripheral blood HBG2 4.49 0.05 [39]25 human HT-induced coronary disease norm peripheral blood HBM 5.33 0.05 [39]26 human HT-induced coronary disease norm peripheral blood HBQ1 3.10 0.05 [39]27 human HT-induced atrial fibrillation norm auricle tissue biopsy HBA2 2.37 10−2 [39]

28 rat ISIAH WAG brain stem Pcdhb7 1.60 10−2 [43]29 mouse BPH/2J BPN/3J kidneys Pcdhb16 1.22 10−3 [54]

30 human pulmonary hypertension duringpulmonary fibrosis norm lungs PCDHB10 1.89 10−2 [30]

31 human pulmonary hypertension duringpulmonary fibrosis norm lungs PCDHB15 1.47 10−4 [30]

32 human pulmonary hypertension duringpulmonary fibrosis norm lungs PCDHB16 1.38 10−4 [30]

33 human pulmonary hypertension duringpulmonary fibrosis norm lungs PCDHB17P 1.21 10−2 [30]

34 human pulmonary hypertension duringpulmonary fibrosis norm lungs PCDHB4 2.93 10−4 [30]

35 human pulmonary hypertension duringpulmonary fibrosis norm lungs PCDHB6 1.35 10−2 [30]

36 human HT-induced coronary disease norm peripheral blood PCDHB11 1.12 0.05 [39]37 human HT-induced coronary disease norm peripheral blood PCDHB13 1.04 0.05 [39]

Notes. HT, hypertension.

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Table 8. The investigated genome-wide RNA-Seq transcriptomes (of domestic animals with theirwild congeners) that we could find in the PubMed database [69].

# Domestic Animals Wild Animals Tissue NDEG Ref.

1 tame rats aggressive rats hypothalamus 46 [72]2 tame rats aggressive rats frontal cortex 20 [187]3 guinea pigs cavy frontal cortex 883 [187]4 domestic rabbits wild rabbits frontal cortex 17 [187]5 domestic rabbits wild rabbits parietal-temporal cortex 216 [188]6 domestic rabbits wild rabbits amygdala 118 [188]7 domestic rabbits wild rabbits hypothalamus 43 [188]8 domestic rabbits wild rabbits hippocampus 100 [188]9 dogs wolves blood 450 [189]

10 dogs wolves frontal cortex 13 [187]11 tame foxes aggressive foxes pituitary 327 [190]12 pigs boars frontal cortex 30 [187]13 pigs boars frontal cortex 34 [191]14 pigs boars pituitary 22 [192]15 domestic chicken wild chicken pituitary 474 [193]

Σ 7 domestic animal species 7 wild animal species 8 tissues 2393

Using the 42 DEGs (from the hippocampus of the tame versus aggressive rats) identi-fied here (Table 2), together with these 2393 DEGs of domestic versus wild animals (Table 8),we revealed three β-protocadherin DEGs and seven hemoglobin subunit DEGs, whichare compared in Table 9 with the human homologous genes (HBB, HBD, and PCDHB9),annotated with respect to hypertension in Table S3. Within columns viii and ix of thistable, we transformed the log2 value characterizing the animal hemoglobin subunit andβ-protocadherin DEGs into either underexpression or overexpression of the correspondinggene during divergence of domestic and wild animals from their most recent commonancestor, which is the most widely used phylogeny concept [194–198]. Downregulationof human genes HBB and HBD reduces blood viscosity [199] and corresponds to down-regulation of the homologous genes Hbb-b1, Hbbl, Hba1, Hbad, Hbm, and Hbz1 in the tamerat, domestic chicken, or dog during their divergence from their most recent ancestorswith respect to their wild congeners (Table 9). As for human hemoglobin upregulation, ahigh-altitude environment provokes both hypertension and hyperhemoglobinemia [103].This hemoglobin upregulation in humans corresponds to high hemoglobin subunit levelsin aggressive rats [72], wolves [189], and wild chickens [193] during their microevolution(Table 9). Likewise, human gene PCDHB9 (protocadherin β9) downregulation leads to awide vascular inner diameter [200] and corresponds to downregulation of β-protocadherinsin tame rats [72] and domestic rabbits [188] during their microevolution (Table 9). Finally,PCDHB9 upregulation in humans elevates the risk of gastric cancer [201] (the surgicalremoval of which leads to hypertensive remission [12]) and corresponds to upregulation ofβ-protocadherins in aggressive rats [72] and wild rabbits [188] during their microevolution(Table 9). As a standard Fisher’s 2 × 2 table, Table 10 summarizes the observations detailedin Table 9.

As one can see in Table 10, downregulation of the genes of β-protocadherins andhemoglobin subunits, which were associated with a wide vascular inner diameter [200]and low blood viscosity [199], respectively, was observed only in domestic animals (not intheir wild congeners). This difference is statistically significant according to the binomialdistribution (p < 0.0001), Pearson’s χ2 test (p < 0.001), and Fisher’s exact test (p < 0.001).Thus, downregulation of β-protocadherins and downregulation of hemoglobin subunitsin animals are molecular markers of low stress reactivity [24], which is both a key physio-logical trait for domestic animals [61,62] and a clinically proven hypertension theranosticphysiological marker in everyone, everywhere, anytime [23].

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Table 9. Comparing the effects of changes to the expression of homologous genes (a) on hypertension development in humans and (b) during the divergence ofdomestic and wild animals from their most recent common ancestors.

(a) Humans (b) Animals

Gene

Effect of Gene Expression Changes on Hypertension (HT):Hypertensive (→) or Normotensive (←) RNA-Seq Effect of Gene Expression Changes during Divergence

from the Most Recent Common Ancestor Ref.Downregulation HT Upregulation HT DEG log2 Downregulation Upregulation Tissue

i ii iii iv v vi vii viii ix x xi

HBB, HBDlow blood

viscosity [199]←

high-altitude environmentprovokes

hyperhemoglobinemia andhypertension [103]

→

Hbb-b1 −6.19 tame rat aggressive rat hippocampus [this work]Hbb-b1 −3.97 tame rat aggressive rat hypothalamus [72]

Hbbl −5.92 dogs wolves blood [189]Hba1 −4.06 dogs wolves blood [189]Hbad −1.07 domestic chickens wild chickens pituitary [193]Hbm −6.46 dogs wolves blood [189]Hbz1 −7.10 dogs wolves blood [189]

PCDHB9wide vascular

innerdiameter [200]

←higher risks of gastric

cancer [93], surgical removalof which relieveshypertension [12]

→

Pcdhb9 −1.03 tame rat aggressive rat hippocampus [this work]Pcdhb9 −1.01 tame rat aggressive rat hypothalamus [72]

Pcdhb15 −1.04 domestic rabbits wild rabbits parietal-temporalcortex [188]

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Table 10. Correlations between the effects of unidirectional changes in the expression of homologousgenes (a) on human hypertension and (b) during the divergence of studied domestic and wild animalsfrom their most recent common ancestor.

(b) Animals(a) Humans

Effect of Expression Changes of GenesEncoding Hemoglobin Subunits and

β-Protocadherins in PatientsBinomial

Distribution

Pearson’sχ2 Test Fisher’s

Exact TestHypertensive Normotensive χ2 p

Effect of expression changes ofgenes encoding hemoglobin

subunits and β-protocadherinsduring animal microevolution

wild 10 0 10−420.00 10−3 10−5

domestic 0 10 10−4

3. Discussion

Here, we observed for the first time that downregulation of hemoglobin subunits orβ-protocadherins corresponds to low blood viscosity or a wide vascular inner diameter,i.e., two universal genome-wide hypertension theranostic molecular markers applicable toeveryone, everywhere, anytime, as readers can see in Table 7. Because of atherosclerosiscomorbid with hypertension, this may support our previous finding that natural selectionagainst underexpression of atheroprotective genes slows atherogenesis [202].

Nevertheless, it seems to be highly debatable how low expression levels of humangenes HBB, HBD, and PCDHB9 would be adaptive under natural selection, favoring theirdownregulation that could cause their loss. For this reason, here, we analyzed these genesusing our web service SNP_TATA_Comparator [203] applicable to research on hypertension,owing to its successful use in a clinical study on pulmonary tuberculosis [204] comorbidwith hypertension [205]. Figure S2 exemplifies how we also used the UCSC Browser [206],Bioperl toolkit [207], and a package of R [208], together with both Ensembl [209] anddbSNP [210] databases in the case of the candidate SNP marker (rs34166473) reducingblood viscosity via HBD downregulation [199], as outlined here (Table S4). In total, weexamined all 85 SNPs within the 70 bp proximal promoters of the genes HBD, HBD, andPCDHB9 within build #153 of the dbSNP database [210]. As a result of this work, wefound 27 candidate SNP markers of hypertension, as indicated [12,103,199–201,211] inTable S4 and described [212–220] in Section S1 “Supplementary methods for DNA sequenceanalysis” (see Supplementary Materials).

Besides this, Figure S3 (hereinafter: see Supplementary Materials) presents the selectiveexperimental verification [221–223] of these estimates (in an electrophoretic mobility shiftassay; EMSA) exemplified by minor allele−30C of rs1473693473 (see Section S2 “Supplemen-tary methods for in vitro measurements”). In total, we verified two ancestral alleles of the hu-man HBB and HBD genes along with nine minor alleles, namely: rs35518301:g, rs34166473:c,rs34500389:t, rs33980857:a, rs34598529:g, rs33931746:g, rs33931746:c, rs281864525:c, andrs63750953:deletion (Table S5 (hereinafter: see Supplementary Materials)). Accordingto Goodman–Kruskal generalized correlation (γ), Pearson’s linear correlation (r), andSpearman’s (R) and Kendall’s (τ) rank correlations, our computational predictions andexperimental measurements are in significant agreement with one another (Figure S3c).

Finally, according to the semicentennial tradition, to assess the relative mutationrates (e.g., transitions versus transversions [224], synonymous versus non-synonymoussubstitutions [225], and insertions versus deletions [226]), we compared the genes HBB,HBD, and PCDHB9 in question with the human genome as a whole [227–229] (Table 11).

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Table 11. The hypertension-related candidate SNP markers within HBB, HBD, and PCDHB9 promot-ers (predicted here) and their comparison with genome-wide patterns.

Data: GRCh38, dbSNP rel. 153 [210] H0: Neutral Drift [229,230] H0: “→HT and←HTEquivalence”

SNPs NGENE NSNP NRES N> N< p(H0: N> < N<) [227] N→HT N←HTp(H0: N→HT ≡

N←HT )

Whole-genome norm forSNPs of TBP sites [228] 104 105 103 200 800 >0.99 - - -

HT-related candidate SNPmarkers at TBP sites

[this work]3 85 27 8 19 >0.99 8 19 <0.05

Notes. Hypertension (HT): normotensive (←HT) and hypertensive (→HT). NGENE and NSNP: total numbers ofthe human genes and of their SNPs meeting the criteria for this study. NRES: the total number of the candidate SNPmarkers that can increase (N>) or decrease (N<) the affinity of TATA-binding protein (TBP) for these promotersand to respectively affect the expression of these genes. N←HT and N→HT: total numbers of the candidate SNPmarkers that can prevent or provoke hypertension. p(H0): the estimate of probability for the acceptance of this H0hypothesis, in accordance with the binomial distribution. TBP-site: TATA-binding-protein binding site.

At the top of this table is a genome-wide SNP pattern of TBP sites—where SNPs de-creasing the TBP–DNA affinity dominate over SNPs, thus increasing this affinity within thehuman genome—as predicted by taking into account many mutagenesis molecular mecha-nisms (e.g., epistatic effects) [227] and as proven within the “1000 Genomes” project [228].In accordance with Haldane’s dilemma [229] and neutral evolution theory [230], this whole-genome trait reflects neutral mutation drift as a norm. At the bottom of Table 11 is thehypertension-related candidate SNP markers identified here, which often significantlyreduce the affinity of TBP for promoters of the genes HBB, HBD, and PCDHB9, representingthe genome-wide neutral mutational drift antagonizing hypertension.

Altogether, the hypertension-related candidate SNP markers discussed above fit thenewest concept [231]: in addition to the accumulation of degenerative SNPs owing to theiruncontrollability during neutral mutational drift, some adaptive SNPs can also accumulatein this way (Table 11).

4. Materials and Methods4.1. Animals

The study was conducted on adult male gray rats (R. norvegicus) artificially bred forover 90 generations for either aggressive or tame behavior (as two outbred strains). Therats were kept under standard conditions of the Conventional Animal Facility at the ICGSB RAS (Novosibirsk, Russia), as described elsewhere [64,74,232]. The total number of ratswas 22 (11 aggressive and 11 tame ones), each four months old and weighing 250–270 g,all from different unrelated litters. All the rats were decapitated. Using a handbooktechnique [233], we excised samples of the hippocampus, which were then flash-frozenin liquid nitrogen and stored at −70 ◦C until use. Every effort was made to minimize thenumber of animals under study and to prevent their suffering. This work was conductedin accordance with the guidelines of the Declaration of Helsinki, Directive 2010/63/EU ofthe European Parliament, and of the European Council resolution of 22 September 2010.

The research protocol was approved by the Interinstitutional Commission on Bioethicsat the ICG SB RAS, Novosibirsk, Russia (approval documentation no. 8 dated 19 March2012).

4.2. RNA-Seq

Total RNA was isolated from ~100 mg of the hippocampus tissue samples of tame(n = 3) and aggressive (n = 3) rats using the TRIzol™ reagent (Invitrogen, Carlsbad, CA,USA). The quality of the total-RNA samples was evaluated using a Bioanalyzer 2100(Agilent, Santa-Clara, CA, USA). Samples with optimal RNA Integrity Numbers (RINs)

Int. J. Mol. Sci. 2022, 23, 2835 15 of 28

were chosen for further analysis. Additionally, the total RNA was analyzed quantitativelyon an Invitrogen Qubit™ 2.0 fluorometer (Invitrogen). Different RNA types were separatedwith the mirVana™ Kit (Thermo Fisher Scientific, Waltham, MA, USA). The DynabeadsmRNA Purification Kit (Invitrogen) was used to prepare highly purified mRNA from5 µg of the RNA fraction depleted of small RNAs. Preparation of RNA-seq libraries from15–30 ng of an mRNA fraction was carried out using the ScriptSeq™ v2 RNA-Seq LibraryPreparation Kit (epicenter®, Madison, WI, USA). The quality of the obtained libraries waschecked on a Bioanalyzer 2100. After normalization, barcoded libraries were pooled andhanded over to the Multi-Access Center of Genomic Research (ICG SB RAS, Novosibirsk,Russia) for sequencing on an Illumina NextSeq 550 instrument in a NextSeq® 500/550 HighOutput Kit v2 cassette (75 cycles) under the assumption of a direct read of 75 nucleotides,with at least 40 million reads.

4.3. Mapping of RNA Sequences to the R. norvegicus Reference Genome

First, the primary raw Fastq files were checked by means of a quality control toolFastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc; accessed on 19 De-cember 2018) for high-throughput sequencing data. After that, using the Trimmomatictool [234], we improved the quality of the raw reads step-by-step as follows: (i) removinga base from either the start or end position if the quality was low, (ii) trimming bases bya sliding-window method, and (iii) removing any remaining reads that were less than36 bases long. Next, with the help of the TopHat2 toolbox [235], we aligned the trimmedreads to the R. norvegicus reference genome (RGSC Rnor_6.0, UCSC version Rn6, July2014 assembly). Then, in SAMTools version 1.4 [236], we reformatted these alignmentsinto sorted BAM files. After that, using the htseq-count tool from preprocessing softwareHTSeq v.0.7.2 [237], along with gtf files carrying coordinates of the rat genes accordingto Rnor_6.0 and an indexed SAM file, we assigned the reads in question to these genes.Finally, in DESeq2 [238] via Web service IRIS (http://bmbl.sdstate.edu/IRIS/; accessedon 16 January 2020), we rated the differential expression of the abovementioned rat genes,and to minimize false-positive error rates, applied Fisher’s Z-test [239] with Benjamini’scorrection for multiple comparisons, as well as discarded all the hypothetical, tentative,predicted, uncharacterized, and protein-non-coding genes.

4.4. qPCR

To selectively and independently verify the tame-versus-aggressive rat hippocampalDEGs found here (Table 2), in this work, we performed a qPCR control assay on the totalRNA taken only from the remaining samples of the hypothalamus of tame (n = 8) andaggressive (n = 8) rats. First, with the help of TRIzol™, we isolated total RNA, purified iton Agencourt RNAClean XP Kit magnetic beads (Beckman, #A63987), and quantified itby means of a Qubit™ 2.0 fluorometer (Invitrogen/Life Technologies) along with an RNAHigh-Sensitivity Kit (Invitrogen, cat. # Q32852). After that, we synthesized cDNA usingthe Reverse Transcription Kit (Syntol, #OT-1). Next, using web service PrimerBLAST [240],we designed oligonucleotide primers for qPCR (Table 12).

After that, we carried out qPCR on a LightCycler® 96 (Roche, Basel, Basel-Stadt,Switzerland) with the EVA Green I Kit in three technical replicates. We determined theqPCR efficiency by means of serial cDNA dilutions (standards). In line with the commonlyaccepted recommendations [76], we simultaneously analyzed four reference genes, namely:B2m (β-2-microglobulin) [241], Hprt1 (hypoxanthine phosphoribosyltransferase 1) [242],Ppia (peptidylprolyl isomerase A) [243], and Rpl30 (ribosomal protein L30) [244].

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Table 12. qPCR primers selected using publicly available Web service PrimerBLAST [240].

No. Gene Forward, 5′→3′ Reverse, 5′→3′

DEGs identified in hippocampus of tame versus aggressive adult male rats [this work]1 Ascl3 CCTCTGCTGCCCTTTTCCAG ACTTGACTCGCTGCCTCTCT2 Defb17 TGGTAGCTTGGACTTGAGGAAAGAA TGCAGCAGTGTGTTCCAGGTC

Reference genes3 B2m GTGTCTCAGTTCCACCCACC TTACATGTCTCGGTCCCAGG4 Hprt1 TCCCAGCGTCGTGATTAGTGA CCTTCATGACATCTCGAGCAAG5 Ppia TTCCAGGATTCATGTGCCAG CTTGCCATCCAGCCACTC6 Rpl30 CATCTTGGCGTCTGATCTTG TCAGAGTCTGTTTGTACCCC

Notes. Regarding the DEGs subjected to this qPCR verification, see Table 2; reference rat genes: B2m, β-2-microglobulin [241]; Hprt1, hypoxanthine phosphoribosyltransferase 1 [242]; Ppia, peptidylprolyl isomeraseA [243]; Rpl30, ribosomal protein L30 [244].

4.5. DEGs under Study

In this work, we analyzed all the publicly available independent experimental RNA-Seq datasets—on transcriptomes from the tissues of hypertensive versus normotensivepatients [26–39], hypertensive versus normotensive animals [7,40–57], and domestic versuswild animals [72,176–193].

4.6. Human Genes under Study

Here, we analyzed the 42 human genes that are orthologous to the 42 hippocampalDEGs of the tame versus aggressive rats (Table 2). Using the PubMed database [69], wecharacterized each of these 42 human genes in terms of what is already clinically knownabout how their underexpression or overexpression can manifest itself in hypertension(Table 9 and Tables S3 and S4).

4.7. DNA Sequences under Study

For in silico analysis of the human genes encoding candidate molecular markers forhypertension that were for the first time suggested in this work, we retrieved both DNAsequences and SNPs of their 70 bp proximal promoters from the Ensembl database [209] andfrom the dbSNP database [210], respectively, relative to reference human genome assemblyGRCh38/hg38 using the UCSC Genome Browser [206] in the dialog mode and additionallyby means of toolbox BioPerl [207] in the automated mode, as shown in Figure S2.

4.8. In Silico Analysis of DNA Sequences

We examined SNPs within DNA sequences using our previously developed publicweb service SNP_TATA_Comparator [203], which applies our bioinformatic model of three-step binding between TBP and a human gene promoter, as detailed in the SupplementaryMaterials (i.e., Section S1 “Supplementary methods for DNA sequence analysis”) andadditionally exemplified in Figure S2.

4.9. In Vitro Measurements

In this project, we in vitro measured KD values expressed in “moles per liter” unitsof the equilibrium dissociation constant of TBP promoter complexes by means of theEMSA, for each of the nine chosen candidate SNP markers for hypertension subjected tothis experimental verification—i.e., rs35518301:g, rs34166473:c, rs34500389:t, rs33980857:a,rs34598529:g, rs33931746:g, rs33931746:c, rs281864525:c, and rs63750953:deletion—as de-scribed in-depth in the Supplementary Materials (i.e., Section S2 “Supplementary methodsfor in vitro measurement”).

4.10. Knowledge Base on Domestic Animals’ DEGs with Orthologous Human Genes that CanAffect Hypertension

In files with the flat Excel-compatible textual format, here, on the one hand, we firstdocumented all the suggested associations between DEGs (of domestic versus wild animals)

Int. J. Mol. Sci. 2022, 23, 2835 17 of 28

homologous to the 42 DEGs (in the hippocampus of tame and aggressive rats) identified inthis study. On the other hand, we documented how underexpression or overexpressionof the human genes homologous to these hippocampal rat DEGs can affect hypertension.Next, using the MariaDB 10.2.12 web environment (MariaDB Corp AB, Espoo, Finland), weadded the current findings to our previously created PetDEGsDB knowledge base, whichis publicly available at www.sysbio.ru/domestic-wild (accessed on 16 January 2020).

4.11. Statistical Analysis

Using the options in the standard toolbox of Statistica (StatsoftTM), we applied theMann–Whitney U test, Fisher’s Z-test, Pearson’s linear correlation test, the Goodman–Kruskal generalized correlation test, Spearman’s and Kendall’s rank correlation tests,Pearson’s χ2 test, Fisher’s exact test, and binomial-distribution analysis.

Besides this, using the PAST4.04 software package [77], we conducted principal com-ponent analysis in the Bootstrap-refinement mode via its mode selection path “Multivariate”→ “Ordination”→ “Principal Components (PCA)”→ “Correlation”→ “Bootstrap.”

5. Conclusions

First of all, in this work, we performed high-throughput sequencing of the hippocam-pus transcriptome for three tame adult male rats compared with three aggressive ones (allunrelated animals). The primary experimental data are publicly available for those whowould like to use them (NCBI SRA database ID: PRJNA668014) [75].

With the help of this transcriptome, we found the 42 hippocampal DEGs—in the tameversus aggressive rats in question—with statistical significance (PADJ < 0.05, Fisher’s Z-testwith Benjamini’s correction for multiple comparisons) that was conventionally acceptable(Table 2). Moreover, we selectively validated these DEGs by independent experimentalanalyses (qPCR) of the other eight tame versus eight aggressive adult male rats fromdifferent unrelated litters of the same two outbred strains (Table 3 and Figure 1).

Besides this, using these 42 hippocampal tame-versus-aggressive rat DEGs, whichreflect rat stress reactivity, we meta-analyzed (by homology) all the highly specific DEGs—of hypertensive versus normotensive subjects (i.e., patients and animals)—that we couldfind within mainstream hypertension-related transcriptomic research articles. First, wefound significant correlations between stress reactivity-related and hypertension-relatedconventional log2 values (fold changes) of the homologous DEGs analyzed. Next, wefound principal components, PC1 and PC2, corresponding to a half-difference and half-sum of these log2 values. Finally, these data pointed to downregulation of hemoglobinor β-protocadherins, corresponding to low blood viscosity [199] or a wide vascular innerdiameter [200], as two hypertension theranostic molecular markers applicable to everyone,everywhere, anytime.

Supplementary Materials: Supplementary materials can be found at https://www.mdpi.com/article/10.3390/ijms23052835/s1.

Author Contributions: Conceptualization and supervision, N.A.K.; methodology, A.M., M.N., O.R.and N.G.K.; investigation, I.C., R.K., N.V.K. and S.S.; software, A.B.; validation, E.S. and P.P.; resources,D.O.; data curation, K.Z. and B.K.; writing—original draft preparation, M.P. All authors have readand agreed to the published version of the manuscript.

Funding: This study was supported by the Russian Science Foundation (project no. 19-74-10041).

Institutional Review Board Statement: This work was conducted in accordance with the guidelinesof the Declaration of Helsinki, Directive 2010/63/EU of the European Parliament, and of the EuropeanCouncil resolution of 22 September 2010. The research protocol was approved by the InterinstitutionalCommission on Bioethics at the ICG SB RAS, Novosibirsk, Russia (Approval documentation no.8 dated 19 March 2012).

Informed Consent Statement: Not applicable.

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Data Availability Statement: The primary RNA-Seq data obtained in this work were deposited inthe NCBI SRA database (ID = PRJNA668014).

Acknowledgments: We are grateful to Nikolai Shevchuk (Shevchuk Editing Co., Brooklyn, NY,USA) for fruitful discussions of the work and assistance with adapting it to the requirements ofthe English language for scientific publications. We are also thankful to the Multi-Access Center“Bioinformatics” for the use of computational resources as supported by Russian government projectFWNR-2022-0020. We are appreciative of the Center for Genomic Research at the ICG SB RAS, whereRNA-Seq was carried out, as supported by the Russian Federal Science and Technology Program forthe Development of Genetic Technologies.

Conflicts of Interest: The authors declare no conflict of interest.

Abbreviations

DEG differentially expressed geneEMSA electrophoretic mobility shift assayHT hypertensionlog2 value log2-transformed gene expression fold changePC1 (PC2) major (minor) principal componentqPCR quantitative polymerase chain reactionRNA-Seq RNA sequencingSNP single-nucleotide polymorphismTBP TATA-binding protein

References1. Alpsoy, S. Exercise and hypertension. Adv. Exp. Med. Biol. 2020, 1228, 153–167. [PubMed]2. Barquera, S.; Pedroza-Tobias, A.; Medina, C.; Hernandez-Barrera, L.; Bibbins-Domingo, K.; Lozano, R.; Moran, A.E. Global

overview of the epidemiology of atherosclerotic cardiovascular disease. Arch. Med. Res. 2015, 46, 328–338. [CrossRef] [PubMed]3. Meade, R.D.; Akerman, A.P.; Notley, S.R.; McGinn, R.; Poirier, P.; Gosselin, P.; Kenny, G.P. Physiological factors characterizing

heat-vulnerable older adults: A narrative review. Environ. Int. 2020, 144, 105909. [CrossRef]4. Samanic, C.M.; Barbour, K.E.; Liu, Y.; Wang, Y.; Fang, J.; Lu, H.; Schieb, L.; Greenlund, K.J. Prevalence of self-reported hypertension

and antihypertensive medication use by county and rural-urban classification-United States, 2017. MMWR-Morb. Mortal. Wkly.Rep. 2020, 69, 533–539. [CrossRef]

5. Karaduman, M.; Aparci, M.; Unlu, M.; Ozturk, C.; Balta, S.; Celik, T. Role of screening tests in the detection and management ofblood pressure abnormalities among young population. Angiology 2017, 68, 441–446. [CrossRef]

6. Huang, Q.T.; Chen, J.H.; Hang, L.L.; Liu, S.S.; Zhong, M. Activation of PAR-1/NADPH oxidase/ROS signaling pathways iscrucial for the thrombin-induced sFlt-1 production in extravillous trophoblasts: Possible involvement in the pathogenesis ofpreeclampsia. Cell. Physiol. Biochem. 2015, 35, 1654–1662. [CrossRef]

7. Tharmalingam, S.; Khurana, S.; Murray, A.; Lamothe, J.; Tai, T.C. Whole transcriptome analysis of adrenal glands from prenatalglucocorticoid programmed hypertensive rodents. Sci. Rep. 2020, 10, 18755. [CrossRef] [PubMed]

8. Mardenkyzy, D.; Rakhimzhanova, R.; Dautov, T.; Jongmin, L.; Saduakasova, A.; Kozhakhmetova, Z.; Yelshibaeyva, E. Possibilitiesof computer tomography in the diagnosis of pulmonary hypertension and influence of various factors (gender, age, body weight,reception of medicines) on the severity of the pulmonary hypertension syndrome. Georgian Med. News 2020, 303, 67–72.

9. Khan, A.W.; Olds, G.; Malik, F.; Teran, P.; Hall, N.; Ali, M. Acute myeloid leukemia masquerading as idiopathic intracranialhypertension: A rare initial presentation. Kans. J. Med. 2021, 14, 133–135. [CrossRef]

10. Adams-Campbell, L.L.; Taylor, T.; Hicks, J.; Lu, J.; Dash, C. The effect of a 6-month exercise intervention trial on allostatic load inblack women at increased risk for breast cancer: The FIERCE study. J. Racial Ethn. Health Disparities 2021. [CrossRef] [PubMed]

11. Gilard, V.; Ferey, J.; Marguet, F.; Fontanilles, M.; Ducatez, F.; Pilon, C.; Lesueur, C.; Pereira, T.; Basset, C.; Schmitz-Afonso, I.;et al. Integrative metabolomics reveals deep tissue and systemic metabolic remodeling in glioblastoma. Cancers 2021, 13, 5157.[CrossRef] [PubMed]

12. Cheng, Y.X.; Peng, D.; Tao, W.; Zhang, W. Effect of oncometabolic surgery on gastric cancer: The remission of hypertension, type2 diabetes mellitus, and beyond. World J. Gastrointest. Oncol. 2021, 13, 1157–1163. [CrossRef]

13. Palacios, D.A.; Zabor, E.C.; Munoz-Lopez, C.; Roversi, G.; Mahmood, F.; Abramczyk, E.; Kelly, M.; Wilson, B.; Abouassaly, R.;Campbell, S.C. Does reduced renal function predispose to cancer-specific mortality from renal cell carcinoma? Eur. Urol. 2021, 79,774–780. [CrossRef] [PubMed]

14. Qi, X.L. Current status and prospects of clinical research on portal hypertension in China. Zhonghua Gan Zang Bing Za Zhi (Chin. J.Hepatol.) 2021, 29, 817–819.

Int. J. Mol. Sci. 2022, 23, 2835 19 of 28

15. Saltalamacchia, G.; Frascaroli, M.; Bernardo, A.; Quaquarini, E. Renal and cardiovascular toxicities by new systemic treatmentsfor prostate cancer. Cancers 2020, 12, 1750. [CrossRef] [PubMed]

16. Gu, D.; Fang, D.; Zhang, M.; Guo, J.; Ren, H.; Li, X.; Zhang, Z.; Yang, D.; Zou, X.; Liu, Y.; et al. Gastrin, via activation of PPARα,protects the kidney against hypertensive injury. Clin. Sci. 2021, 135, 409–427. [CrossRef]

17. Chou, L.M.; Beyer, M.M.; Butt, K.M.; Manis, T.; Friedman, E.A. Hypertension jeopardizes diabetic patients following renaltransplant. Trans. Am. Soc. Artif. Intern. Organs 1984, 30, 473–478. [PubMed]

18. Yousaf, M.; Ayasse, M.; Ahmed, A.; Gwillim, E.C.; Janmohamed, S.R.; Yousaf, A.; Patel, K.R.; Thyssen, J.P.; Silverberg, J.I.Association between atopic dermatitis and hypertension: A systematic review and meta-analysis. Br. J. Dermatol. 2021. [CrossRef]

19. Lukawski, K.; Czuczwar, S.J. Assessment of drug-drug interactions between moxonidine and antiepileptic drugs in the maximalelectroshock seizure test in mice. Basic Clin. Pharmacol. Toxicol. 2021, 130, 28–34. [CrossRef] [PubMed]

20. Dinc, H.O.; Saltoglu, N.; Can, G.; Balkan, I.I.; Budak, B.; Ozbey, D.; Caglar, B.; Karaali, R.; Mete, B.; Tuyji Tok, Y.; et al. InactiveSARS-CoV-2 vaccine generates high antibody responses in healthcare workers with and without prior infection. Vaccine 2022, 40,52–58. [CrossRef] [PubMed]

21. Page, I.H. The Mosaic Theory of arterial hypertension-: Its interpretation. Perspect. Biol. Med. 1967, 10, 325–333. [CrossRef][PubMed]

22. Arishe, O.O.; Priviero, F.; Wilczynski, S.A.; Webb, R.C. Exosomes as intercellular messengers in hypertension. Int. J. Mol. Sci.2021, 22, 11685. [CrossRef]

23. Turner, A.I.; Smyth, N.; Hall, S.J.; Torres, S.J.; Hussein, M.; Jayasinghe, S.U.; Ball, K.; Clow, A.J. Psychological stress reactivity andfuture health and disease outcomes: A systematic review of prospective evidence. Psychoneuroendocrinology 2020, 114, 104599.[CrossRef] [PubMed]

24. Cannon, W.B. The emergency function of the adrenal medulla in pain and the major emotions. Am. J. Physiol. 1914, 33, 356–372.[CrossRef]

25. Poulter, N.R.; Prabhakaran, D.; Caulfield, M. Hypertension. Lancet 2015, 386, 801–812. [CrossRef]26. Wu, Y.B.; Zang, W.D.; Yao, W.Z.; Luo, Y.; Hu, B.; Wang, L.; Liang, Y.L. Analysis of FOS, BTG2, and NR4A in the function of renal

medullary hypertension. Genet. Mol. Res. 2013, 12, 3735–3741. [CrossRef] [PubMed]27. Qiu, X.; Lin, J.; Liang, B.; Chen, Y.; Liu, G.; Zheng, J. Identification of hub genes and microRNAs associated with idiopathic

pulmonary arterial hypertension by integrated bioinformatics analyses. Front. Genet. 2021, 12, 667406. [CrossRef] [PubMed]28. Stearman, R.S.; Bui, Q.M.; Speyer, G.; Handen, A.; Cornelius, A.R.; Graham, B.B.; Kim, S.; Mickler, E.A.; Tuder, R.M.; Chan,

S.Y.; et al. Systems analysis of the human pulmonary arterial hypertension lung transcriptome. Am. J. Respir. Cell Mol. Biol. 2019,60, 637–649. [CrossRef] [PubMed]

29. Li, C.; Zhang, Z.; Xu, Q.; Wu, T.; Shi, R. Potential mechanisms and serum biomarkers involved in sex differences in pulmonaryarterial hypertension. Medicine 2020, 99, e19612. [CrossRef] [PubMed]

30. Yao, X.; Jing, T.; Wang, T.; Gu, C.; Chen, X.; Chen, F.; Feng, H.; Zhao, H.; Chen, D.; Ma, W. Molecular characterization andelucidation of pathways to identify novel therapeutic targets in pulmonary arterial hypertension. Front. Physiol. 2021, 12, 694702.[CrossRef]

31. Awad, K.S.; Elinoff, J.M.; Wang, S.; Gairhe, S.; Ferreyra, G.A.; Cai, R.; Sun, J.; Solomon, M.A.; Danner, R.L. Raf/ERK drives theproliferative and invasive phenotype of BMPR2-silenced pulmonary artery endothelial cells. Am. J. Physiol. Lung Cell. Mol.Physiol. 2016, 310, L187–L201. [CrossRef]

32. Saei, H.; Govahi, A.; Abiri, A.; Eghbali, M.; Abiri, M. Comprehensive transcriptome mining identified the gene expressionsignature and differentially regulated pathways of the late-onset preeclampsia. Pregnancy Hypertens. 2021, 25, 91–102. [CrossRef][PubMed]

33. Jung, Y.W.; Shim, J.I.; Shim, S.H.; Shin, Y.J.; Shim, S.H.; Chang, S.W.; Cha, D.H. Global gene expression analysis of cell-free RNAin amniotic fluid from women destined to develop preeclampsia. Medicine 2019, 98, e13971. [CrossRef] [PubMed]

34. Textoris, J.; Ivorra, D.; Ben Amara, A.; Sabatier, F.; Menard, J.P.; Heckenroth, H.; Bretelle, F.; Mege, J.L. Evaluation of current andnew biomarkers in severe preeclampsia: A microarray approach reveals the VSIG4 gene as a potential blood biomarker. PLoSONE 2013, 8, e82638. [CrossRef]

35. Yong, H.E.; Melton, P.E.; Johnson, M.P.; Freed, K.A.; Kalionis, B.; Murthi, P.; Brennecke, S.P.; Keogh, R.J.; Moses, E.K. Genome-widetranscriptome directed pathway analysis of maternal pre-eclampsia susceptibility genes. PLoS ONE 2015, 10, e0128230. [CrossRef][PubMed]

36. Ahn, S.; Jeong, E.; Min, J.W.; Kim, E.; Choi, S.S.; Kim, C.J.; Lee, D.C. Identification of genes dysregulated by elevation ofmicroRNA-210 levels in human trophoblasts cell line, Swan 71. Am. J. Reprod. Immunol. 2017, 78, e12722. [CrossRef]

37. Neusser, M.A.; Lindenmeyer, M.T.; Moll, A.G.; Segerer, S.; Edenhofer, I.; Sen, K.; Stiehl, D.P.; Kretzler, M.; Grone, H.J.; Schlondorff,D.; et al. Human nephrosclerosis triggers a hypoxia-related glomerulopathy. Am. J. Pathol. 2010, 176, 594–607. [CrossRef][PubMed]

38. Koper, A.; Zeef, L.A.; Joseph, L.; Kerr, K.; Gosney, J.; Lindsay, M.A.; Booton, R. Whole transcriptome analysis of pre-invasive andinvasive early squamous lung carcinoma in archival laser microdissected samples. Respir. Res. 2017, 18, 12. [CrossRef] [PubMed]

39. Zheng, Y.; He, J.Q. Common differentially expressed genes and pathways correlating both coronary artery disease and atrialfibrillation. EXCLI J. 2021, 20, 126–141.

Int. J. Mol. Sci. 2022, 23, 2835 20 of 28

40. Stefanova, N.A.; Maksimova, K.Y.; Rudnitskaya, E.A.; Muraleva, N.A.; Kolosova, N.G. Association of cerebrovascular dysfunctionwith the development of Alzheimer’s disease-like pathology in OXYS rats. BMC Genom. 2018, 19, 75. [CrossRef] [PubMed]

41. Stefanova, N.A.; Ershov, N.I.; Maksimova, K.Y.; Muraleva, N.A.; Tyumentsev, M.A.; Kolosova, N.G. The rat prefrontal-cortextranscriptome: Effects of aging and sporadic Alzheimer’s disease-like pathology. J. Gerontol. A Biol. Sci. Med. Sci. 2019, 74, 33–43.[CrossRef]

42. Kozhevnikova, O.S.; Korbolina, E.E.; Ershov, N.I.; Kolosova, N.G. Rat retinal transcriptome: Effects of aging and AMD-likeretinopathy. Cell Cycle 2013, 12, 1745–1761. [CrossRef]

43. Fedoseeva, L.A.; Klimov, L.O.; Ershov, N.I.; Efimov, V.M.; Markel, A.L.; Orlov, Y.L.; Redina, O.E. The differences in brain stemtranscriptional profiling in hypertensive ISIAH and normotensive WAG rats. BMC Genom. 2019, 20, 297. [CrossRef]

44. Klimov, L.O.; Ershov, N.I.; Efimov, V.M.; Markel, A.L.; Redina, O.E. Genome-wide transcriptome analysis of hypothalamus in ratswith inherited stress-induced arterial hypertension. BMC Genet. 2016, 17, 13. [CrossRef]

45. Ryazanova, M.A.; Fedoseeva, L.A.; Ershov, N.I.; Efimov, V.M.; Markel, A.L.; Redina, O.E. The gene-expression profile of renalmedulla in ISIAH rats with inherited stress-induced arterial hypertension. BMC Genet. 2016, 17 (Suppl. S3), 151. [CrossRef]

46. Fedoseeva, L.A.; Ryazanova, M.A.; Ershov, N.I.; Markel, A.L.; Redina, O.E. Comparative transcriptional profiling of renal cortexin rats with inherited stress-induced arterial hypertension and normotensive Wistar Albino Glaxo rats. BMC Genet. 2016, 17(Suppl. S1), 12. [CrossRef] [PubMed]

47. Fedoseeva, L.A.; Klimov, L.O.; Ershov, N.I.; Alexandrovich, Y.V.; Efimov, V.M.; Markel, A.L.; Redina, O.E. Molecular determinantsof the adrenal gland functioning related to stress-sensitive hypertension in ISIAH rats. BMC Genom. 2016, 17, 989. [CrossRef][PubMed]

48. Yuan, X.; Wu, Q.; Liu, X.; Zhang, H.; Xiu, R. Transcriptomic profile analysis of brain microvascular pericytes in spontaneouslyhypertensive rats by RNA-Seq. Am. J. Transl. Res. 2018, 10, 2372–2386. [PubMed]

49. Watanabe, Y.; Yoshida, M.; Yamanishi, K.; Yamamoto, H.; Okuzaki, D.; Nojima, H.; Yasunaga, T.; Okamura, H.; Matsunaga, H.;Yamanishi, H. Genetic analysis of genes causing hypertension and stroke in spontaneously hypertensive rats: Gene expressionprofiles in the kidneys. Int. J. Mol. Med. 2015, 36, 712–724. [CrossRef] [PubMed]

50. Xiao, G.; Wang, T.; Zhuang, W.; Ye, C.; Luo, L.; Wang, H.; Lian, G.; Xie, L. RNA sequencing analysis of monocrotaline-inducedPAH reveals dysregulated chemokine and neuroactive ligand receptor pathways. Aging 2020, 12, 4953–4969. [CrossRef] [PubMed]

51. Du, H.; Xiao, G.; Xue, Z.; Li, Z.; He, S.; Du, X.; Zhou, Z.; Cao, L.; Wang, Y.; Yang, J.; et al. QiShenYiQi ameliorates salt-inducedhypertensive nephropathy by balancing ADRA1D and SIK1 expression in Dahl salt-sensitive rats. Biomed. Pharmacother. 2021,141, 111941. [CrossRef] [PubMed]

52. Ashraf, U.M.; Mell, B.; Jose, P.A.; Kumarasamy, S. Deep transcriptomic profiling of Dahl salt-sensitive rat kidneys with mutantform of Resp18. Biochem. Biophys. Res. Commun. 2021, 572, 35–40. [CrossRef] [PubMed]

53. Zhou, X.; Zhang, X.X.; Mahmmod, Y.S.; Hernandez, J.A.; Li, G.F.; Huang, W.Y.; Wang, Y.P.; Zheng, Y.X.; Li, X.M.; Yuan, Z.G. ATranscriptome analysis: Various reasons of adverse pregnancy outcomes caused by acute toxoplasma gondii infection. Front.Physiol. 2020, 11, 115. [CrossRef]

54. Puig, O.; Wang, I.M.; Cheng, P.; Zhou, P.; Roy, S.; Cully, D.; Peters, M.; Benita, Y.; Thompson, J.; Cai, T.Q. Transcriptome profilingand network analysis of genetically hypertensive mice identifies potential pharmacological targets of hypertension. Physiol.Genom. 2010, 42A, 24–32. [CrossRef] [PubMed]

55. Loke, S.Y.; Wong, P.T.; Ong, W.Y. Global gene expression changes in the prefrontal cortex of rabbits with hypercholesterolemiaand/or hypertension. Neurochem. Int. 2017, 102, 33–56. [CrossRef] [PubMed]

56. Park, W.; Rengaraj, D.; Kil, D.Y.; Kim, H.; Lee, H.K.; Song, K.D. RNA-seq analysis of the kidneys of broiler chickens fed dietscontaining different concentrations of calcium. Sci. Rep. 2017, 7, 11740. [CrossRef]

57. Yang, F.; Cao, H.; Xiao, Q.; Guo, X.; Zhuang, Y.; Zhang, C.; Wang, T.; Lin, H.; Song, Y.; Hu, G.; et al. Transcriptome analysis andgene identification in the pulmonary artery of broilers with ascites syndrome. PLoS ONE 2016, 11, e0156045. [CrossRef] [PubMed]

58. Trovato, G.M. Sustainable medical research by effective and comprehensive medical skills: Overcoming the frontiers by predictive,preventive and personalized medicine. EPMA J. 2014, 5, 14. [CrossRef] [PubMed]

59. Osadchuk, A.V.; Markel, A.L.; Khusainov, R.A.; Naumenko, E.V.; Beliaev, D.K. Problems in the genetics of stress. IV. A geneticanalysis of the level of autonomic reactivity in emotional stress in rats. Sov. Genet. 1979, 15, 1847–1857.

60. Markel, A.L. Features of the behavior of the rat with hereditarily determined arterial hypertension. Zhurnal Vyss. Nervn.Deiatelnosti Im. IP Pavlov. 1986, 36, 956–962.

61. Herbek, Y.E.; Zakharov, I.K.; Trapezov, O.V.; Shumny, V.K. Evolution compressed in time. Philos. Sci. 2013, 1, 115–139.62. Oskina, I.N.; Herbeck, Y.E.; Shikhevich, S.G.; Plyusnina, I.Z.; Gulevich, R.G. Alterations in the hypothalamus-pituitary-adrenal

and immune systems during selection of animals for tame behavior. Inf. Bull. VOGiS 2008, 12, 39–49.63. Prasolova, L.A.; Gerbek, Y.E.; Gulevich, R.G.; Shikhevich, S.G.; Konoshenko, M.Y.; Kozhemyakina, R.V.; Oskina, I.N.; Plyusnina,

I.Z. The effects of prolonged selection for behavior on the stress response and activity of the reproductive system of male greymice (Rattus norvegicus). Russ. J. Genet. 2014, 50, 846–852. [CrossRef]

64. Belyaev, D.K.; Borodin, P.M. The influence of stress on variation and its role in evolution. Biol. Zent. 1982, 100, 705–714.65. Kolosova, N.G.; Stefanova, N.A.; Korbolina, E.E.; Fursova, A.Z.; Kozhevnikova, O.S. Senescence-accelerated OXYS rats: A genetic

model of premature aging and age-related diseases. Adv. Gerontol. 2014, 27, 336–340. [CrossRef]

Int. J. Mol. Sci. 2022, 23, 2835 21 of 28

66. Stefanova, N.A.; Kozhevnikova, O.S.; Vitovtov, A.O.; Maksimova, K.Y.; Logvinov, S.V.; Rudnitskaya, E.A.; Korbolina, E.E.;Muraleva, N.A.; Kolosova, N.G. Senescence-accelerated OXYS rats: A model of age-related cognitive decline with relevance toabnormalities in Alzheimer disease. Cell Cycle 2014, 13, 898–909. [CrossRef] [PubMed]

67. Devyatkin, V.A.; Redina, O.E.; Muraleva, N.A.; Kolosova, N.G. Single-nucleotide polymorphisms (SNPs) both associated withhypertension and contributing to accelerated-senescence traits in OXYS rats. Int. J. Mol. Sci. 2020, 21, 3542. [CrossRef] [PubMed]

68. Devyatkin, V.A.; Redina, O.E.; Kolosova, N.G.; Muraleva, N.A. Single-nucleotide polymorphisms associated with the senescence-accelerated phenotype of OXYS rats: A focus on Alzheimer’s disease-like and age-related-macular-degeneration-like pathologies.J. Alzheimer’s Dis. 2020, 73, 1167–1183. [CrossRef] [PubMed]

69. Lu, Z. PubMed and beyond: A survey of web tools for searching biomedical literature. Database 2011, 2011, baq036. [CrossRef][PubMed]

70. Vasiliev, G.; Chadaeva, I.; Rasskazov, D.; Ponomarenko, P.; Sharypova, E.; Drachkova, I.; Bogomolov, A.; Savinkova, L.;Ponomarenko, M.; Kolchanov, N.; et al. A bioinformatics model of human diseases on the basis of differentially expressed genes(of domestic versus wild animals) that are orthologs of human genes associated with reproductive-potential changes. Int. J. Mol.Sci. 2021, 22, 2346. [CrossRef] [PubMed]

71. Klimova, N.V.; Oshchepkova, E.; Chadaeva, I.; Sharypova, E.; Ponomarenko, P.; Drachkova, I.; Rasskazov, D.; Oshchepkov,D.; Ponomarenko, M.; Savinkova, L.; et al. Disruptive selection of human immunostimulatory and immunosuppressive genesboth provokes and prevents rheumatoid arthritis, respectively, as a self-domestication syndrome. Front. Genet. 2021, 12, 610774.[CrossRef] [PubMed]

72. Chadaeva, I.; Ponomarenko, P.; Kozhemyakina, R.; Suslov, V.; Bogomolov, A.; Klimova, N.; Shikhevich, S.; Savinkova, L.;Oshchepkov, D.; Kolchanov, N.A.; et al. Domestication explains two-thirds of differential-gene-expression variance betweendomestic and wild animals; the remaining one-third reflects intraspecific and interspecific variation. Animals 2021, 11, 2667.[CrossRef] [PubMed]

73. Hecht, K.; Hai, N.V.; Hecht, T.; Moritz, V.; Woossmann, H. Correlations between hippocampus function and stressed learning andtheir effect on cerebro-visceral regulation processes. Acta Biol. Med. Ger. 1976, 35, 35–45. [PubMed]

74. Plyusnina, I.; Oskina, I. Behavioral and adrenocortical responses to open-field test in rats selected for reduced aggressivenesstoward humans. Physiol. Behav. 1997, 61, 381–385. [CrossRef]

75. Sayers, E.W.; Beck, J.; Bolton, E.E.; Bourexis, D.; Brister, J.R.; Canese, K.; Comeau, D.C.; Funk, K.; Kim, S.; Klimke, W.; et al.Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2021, 49, D10–D17. [CrossRef]

76. Bustin, S.A.; Benes, V.; Garson, J.A.; Hellemans, J.; Huggett, J.; Kubista, M.; Mueller, R.; Nolan, T.; Pfaffl, M.W.; Shipley, G.L.; et al.The MIQE guidelines: Minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 2009, 55,611–622. [CrossRef] [PubMed]

77. Hammer, O.; Harper, D.A.T.; Ryan, P.D. PAST: Paleontological statistics software package for education and data analysis.Palaeontol. Electron. 2001, 4, 1–9.

78. McNeil, J.B.; Jackson, K.E.; Wang, C.; Siew, E.D.; Vincz, A.J.; Shaver, C.M.; Bastarache, J.A.; Ware, L.B. Linear association betweenhypoalbuminemia and increased risk of acute respiratory distress syndrome in critically ill adults. Crit Care Explor. 2021, 3, e0527.[CrossRef]

79. Facciorusso, A.; Nacchiero, M.C.; Rosania, R.; Laonigro, G.; Longo, N.; Panella, C.; Ierardi, E. The use of human albumin for thetreatment of ascites in patients with liver cirrhosis: Item of safety, facts, controversies and perspectives. Curr. Drug Saf. 2011, 6,267–274. [CrossRef] [PubMed]

80. Vassiliou, A.G.; Keskinidou, C.; Kotanidou, A.; Frantzeskaki, F.; Dimopoulou, I.; Langleben, D.; Orfanos, S.E. Knockdown ofbone morphogenetic protein type II receptor leads to decreased aquaporin 1 expression and function in human pulmonarymicrovascular endothelial cells. Can. J. Physiol. Pharmacol. 2020, 98, 834–839. [CrossRef] [PubMed]

81. Schuoler, C.; Haider, T.J.; Leuenberger, C.; Vogel, J.; Ostergaard, L.; Kwapiszewska, G.; Kohler, M.; Gassmann, M.; Huber, L.C.;Brock, M. Aquaporin 1 controls the functional phenotype of pulmonary smooth muscle cells in hypoxia-induced pulmonaryhypertension. Basic Res. Cardiol. 2017, 112, 30. [CrossRef] [PubMed]

82. Wang, C.Y.; Shahi, P.; Huang, J.T.; Phan, N.N.; Sun, Z.; Lin, Y.C.; Lai, M.D.; Werb, Z. Systematic analysis of the achaete-scutecomplex-like gene signature in clinical cancer patients. Mol. Clin. Oncol. 2017, 6, 7–18. [CrossRef] [PubMed]

83. Martin, T.G.; Tawfik, S.; Moravec, C.S.; Pak, T.R.; Kirk, J.A. BAG3 expression and sarcomere localization in the human heart arelinked to HSF-1 and are differentially affected by sex and disease. Am. J. Physiol. Heart Circ. Physiol. 2021, 320, H2339–H2350.[CrossRef] [PubMed]

84. Derosa, G.; Maffioli, P.; Rosati, A.; Basile, A.; D’Angelo, A.; Romano, D.; Sahebkar, A.; Falco, A.; Turco, M.C. Evaluation of BAG3levels in healthy subjects, hypertensive patients, and hypertensive diabetic patients. J. Cell. Physiol. 2018, 3, 1791–1795. [CrossRef]

85. Wang, Y.P.; Huang, L.Y.; Sun, W.M.; Zhang, Z.Z.; Fang, J.Z.; Wei, B.F.; Wu, B.H.; Han, Z.G. Insulin receptor tyrosine kinasesubstrate activates EGFR/ERK signalling pathway and promotes cell proliferation of hepatocellular carcinoma. Cancer Lett. 2013,337, 96–106. [CrossRef] [PubMed]

86. Park, J.S.; Pierorazio, P.M.; Lee, J.H.; Lee, H.J.; Lim, Y.S.; Jang, W.S.; Kim, J.; Lee, S.H.; Rha, K.H.; Cho, N.H.; et al. Gene expressionanalysis of aggressive clinical T1 stage clear cell renal cell carcinoma for identifying potential diagnostic and prognostic biomarkers.Cancers 2020, 12, 222. [CrossRef]

Int. J. Mol. Sci. 2022, 23, 2835 22 of 28

87. Stojanovic, M.; Goldner, B.; Ivkovic, D. Renal cell carcinoma and arterial hypertension. Clin. Exp. Nephrol. 2009, 13, 295–299.[CrossRef]

88. Han, F.; Zhao, H.; Lu, J.; Yun, W.; Yang, L.; Lou, Y.; Su, D.; Chen, X.; Zhang, S.; Jin, H.; et al. Anti-Tumor effects of BDH1 in acutemyeloid leukemia. Front. Oncol. 2021, 11, 694594. [CrossRef]

89. Camerino, M.; Giacobino, D.; Manassero, L.; Iussich, S.; Riccardo, F.; Cavallo, F.; Tarone, L.; Olimpo, M.; Lardone, E.; Martano, M.;et al. Prognostic impact of bone invasion in canine oral malignant melanoma treated by surgery and anti-CSPG4 vaccination: Aretrospective study on 68 cases (2010–2020). Vet. Comp. Oncol. 2021, 20, 189–197. [CrossRef]

90. El-Qushayri, A.E.; Benmelouka, A.Y.; Salman, S.; Nardone, B. Melanoma and hypertension, is there an association? A U.S.population based study. Ital. J. Dermatol. Venerol. 2021. [CrossRef]

91. Damour, A.; Garcia, M.; Seneschal, J.; Leveque, N.; Bodet, C. Eczema herpeticum: Clinical and pathophysiological aspects. Clin.Rev. Allergy Immunol. 2020, 59, 1–18. [CrossRef] [PubMed]

92. Ohtani, M.; Nishimura, T. Sulfur-containing amino acids in aged garlic extract inhibit inflammation in human gingival epithelialcells by suppressing intercellular adhesion molecule-1 expression and IL-6 secretion. Biomed. Rep. 2020, 12, 99–108. [CrossRef][PubMed]

93. Liu, R.; Zhang, Z.; Liu, H.; Hou, P.; Lang, J.; Wang, S.; Yan, H.; Li, P.; Huang, Z.; Wu, H.; et al. Human β-defensin 2 is a novelopener of Ca2+-activated potassium channels and induces vasodilation and hypotension in monkeys. Hypertension 2013, 62,415–425. [CrossRef] [PubMed]

94. He, L.; Yang, Y.; Chen, J.; Zou, P.; Li, J. Transcriptional activation of ENPP2 by FoxO4 protects cardiomyocytes from doxorubicin-induced toxicity. Mol. Med. Rep. 2021, 24, 668. [CrossRef]

95. Matsumura, N.; Zordoky, B.N.; Robertson, I.M.; Hamza, S.M.; Parajuli, N.; Soltys, C.M.; Beker, D.L.; Grant, M.K.; Razzoli, M.;Bartolomucci, A.; et al. Co-administration of resveratrol with doxorubicin in young mice attenuates detrimental late-occurringcardiovascular changes. Cardiovasc. Res. 2018, 114, 1350–1359. [CrossRef] [PubMed]

96. Xu, X.Y.; Guo, W.J.; Pan, S.H.; Zhang, Y.; Gao, F.L.; Wang, J.T.; Zhang, S.; Li, H.Y.; Wang, R.; Zhang, X. TILRR (FREM1 isoform 2) isa prognostic biomarker correlated with immune infiltration in breast cancer. Aging 2020, 12, 19335–19351. [CrossRef]

97. Li, H.N.; Li, X.R.; Lv, Z.T.; Cai, M.M.; Wang, G.; Yang, Z.F. Elevated expression of FREM1 in breast cancer indicates favorableprognosis and high-level immune infiltration status. Cancer Med. 2020, 9, 9554–9570. [CrossRef]

98. Wang, S.; Zhu, G.; Jiang, D.; Rhen, J.; Li, X.; Liu, H.; Lyu, Y.; Tsai, P.; Rose, Y.; Nguyen, T.; et al. Reduced Notch1 cleavage promotesthe development of pulmonary hypertension. Hypertension 2021, 79, 79–92. [CrossRef]

99. Zhou, C.; Yu, J.; Wang, M.; Yang, J.; Xiong, H.; Huang, H.; Wu, D.; Hu, S.; Wang, Y.; Chen, X.Z.; et al. Identification ofglycerol-3-phosphate dehydrogenase 1 as a tumour suppressor in human breast cancer. Oncotarget 2017, 8, 101309–101324.[CrossRef]

100. Azad, G.K.; Singh, V.; Thakare, M.J.; Baranwal, S.; Tomar, R.S. Mitogen-activated protein kinase Hog1 is activated in response tocurcumin exposure in the budding yeast Saccharomyces cerevisiae. BMC Microbiol. 2014, 14, 317. [CrossRef]

101. Kim, Y.B.; Kim, Y.S.; Kim, W.B.; Shen, F.Y.; Lee, S.W.; Chung, H.J.; Kim, J.S.; Han, H.C.; Colwell, C.S.; Kim, Y.I. GABAergicexcitation of vasopressin neurons: Possible mechanism underlying sodium-dependent hypertension. Circ. Res. 2013, 113,1296–1307. [CrossRef]

102. Triantafyllou, A.I.; Vyssoulis, G.P.; Karpanou, E.A.; Karkalousos, P.L.; Triantafyllou, E.A.; Aessopos, A.; Farmakis, D.T. Impact ofβ-thalassemia trait carrier state on cardiovascular risk factors and metabolic profile in patients with newly diagnosed hypertension.J. Hum. Hypertens. 2014, 28, 328–332. [CrossRef] [PubMed]

103. Zheng, H.; Wei, X.C.; Yu, T.; Lei, Q. Anesthesia of a high-altitude area inhabitant who underwent aortic dissection emergencysurgery in a low-altitude area. J. Int. Med. Res. 2020, 48, 300060520979871. [CrossRef] [PubMed]

104. Chen, M.; Li, M.H.; Zhang, N.; Sun, W.W.; Wang, H.; Wang, Y.A.; Zhao, Y.; Wei, W. Pro-angiogenic effect of exosomal microRNA-103a in mice with rheumatoid arthritis via the downregulation of hepatocyte nuclear factor 4 alpha and activation of theJAK/STAT3 signaling pathway. J. Biol. Regul. Homeost Agents 2021, 35, 629–640. [PubMed]

105. Argnani, L.; Zanetti, A.; Carrara, G.; Silvagni, E.; Guerrini, G.; Zambon, A.; Scire, C.A. Rheumatoid arthritis and cardiovascularrisk: Retrospective matched-cohort analysis based on the RECORD study of the italian society for rheumatology. Front. Med.2021, 8, 745601. [CrossRef] [PubMed]

106. Ni, Z.; Lu, W.; Li, Q.; Han, C.; Yuan, T.; Sun, N.; Shi, Y. Analysis of the HNF4A isoform-regulated transcriptome identifies CCL15as a downstream target in gastric carcinogenesis. Cancer Biol. Med. 2021, 18, 530–546. [CrossRef] [PubMed]

107. Sejourne, J.; Llaneza, D.; Kuti, O.J.; Page, D.T. Social behavioral deficits coincide with the onset of seizure susceptibility in micelacking serotonin receptor 2c. PLoS ONE 2015, 10, e0136494. [CrossRef] [PubMed]

108. Veeramachaneni, G.K.; Thunuguntla, V.B.S.C.; Bhaswant, M.; Mathai, M.L.; Bondili, J.S. Pharmacophore directed screening ofagonistic natural molecules showing affinity to 5HT2C receptor. Biomolecules 2019, 9, 556. [CrossRef] [PubMed]

109. Kelm-Nelson, C.A.; Gammie, S. Gene expression within the periaqueductal gray is linked to vocal behavior and early-onsetparkinsonism in Pink1 knockout rats. BMC Genom. 2020, 21, 625. [CrossRef]

110. Shin, N.Y.; Park, Y.W.; Yoo, S.W.; Yoo, J.Y.; Choi, Y.; Jang, J.; Ahn, K.J.; Kim, B.S.; Kim, J.S. Adverse effects of hypertension,supine hypertension, and perivascular space on cognition and motor function in PD. NPJ Parkinsons Dis. 2021, 7, 69. [CrossRef][PubMed]

Int. J. Mol. Sci. 2022, 23, 2835 23 of 28

111. Zhao, Z.L.; Du, S.; Shen, S.X.; Luo, P.; Ding, S.K.; Wang, G.G.; Wang, L.X. Biomarkers screening for viral myocarditis throughproteomics analysis of plasma exosomes. Zhonghua Yi Xue Za Zhi (Chin. Med. J.) 2019, 99, 343–348.

112. Fox, S.E.; Falgout, L.; Vander Heide, R.S. COVID-19 myocarditis: Quantitative analysis of the inflammatory infiltrate and aproposed mechanism. Cardiovasc. Pathol. 2021, 54, 107361. [CrossRef] [PubMed]

113. Karo-Atar, D.; Moshkovits, I.; Eickelberg, O.; Konigshoff, M.; Munitz, A. Paired immunoglobulin-like receptor-B inhibitspulmonary fibrosis by suppressing profibrogenic properties of alveolar macrophages. Am. J. Respir. Cell Mol. Biol. 2013, 48,456–464. [CrossRef] [PubMed]

114. Huang, J.; Burke, P.S.; Cung, T.D.; Pereyra, F.; Toth, I.; Walker, B.D.; Borges, L.; Lichterfeld, M.; Yu, X.G. Leukocyteimmunoglobulin-like receptors maintain unique antigen-presenting properties of circulating myeloid dendritic cells inHIV-1-infected elite controllers. J. Virol. 2010, 84, 9463–9471. [CrossRef]

115. Kumar, A.; Mahajan, A.; Salazar, E.A.; Pruitt, K.; Guzman, C.A.; Clauss, M.A.; Almodovar, S.; Dhillon, N.K. Impact of humanimmunodeficiency virus on pulmonary vascular disease. Glob. Cardiol. Sci. Pract. 2021, 2021, e202112. [CrossRef]

116. Sakamoto, S.; Matsuura, K.; Masuda, S.; Hagiwara, N.; Shimizu, T. Heart-derived fibroblasts express LYPD-1 and negativelyregulate angiogenesis in rat. Regen. Ther. 2020, 15, 27–33. [CrossRef]

117. Kumar, N.; Verma, R.; Lohana, P.; Lohana, A.; Ramphul, K. Acute myocardial infarction in COVID-19 patients. A review of casesin the literature. Arch. Med. Sci. Atheroscler Dis. 2021, 6, e169–e175. [CrossRef]

118. Matsushita, Y.; Furukawa, T.; Kasanuki, H.; Nishibatake, M.; Kurihara, Y.; Ikeda, A.; Kamatani, N.; Takeshima, H.; Matsuoka, R.Mutation of junctophilin type 2 associated with hypertrophic cardiomyopathy. J. Hum. Genet. 2007, 52, 543–548. [CrossRef]

119. Sridharan, A.; Maron, M.S.; Carrick, R.T.; Madias, C.A.; Huang, D.; Cooper, C.; Drummond, J.; Maron, B.J.; Rowin, E.J. Impact ofcomorbidities on atrial fibrillation and sudden cardiac death in hypertrophic cardiomyopathy. J. Cardiovasc. Electrophysiol. 2021,33, 20–29. [CrossRef]

120. Rozanski, A.; Takano, A.P.; Kato, P.N.; Soares, A.G.; Lellis-Santos, C.; Campos, J.C.; Ferreira, J.C.; Barreto-Chaves, M.L.; Moriscot,A.S. M-protein is down-regulated in cardiac hypertrophy driven by thyroid hormone in rats. Mol. Endocrinol. 2013, 27, 2055–2065.[CrossRef] [PubMed]

121. Grabowski, K.; Herlan, L.; Witten, A.; Qadri, F.; Eisenreich, A.; Lindner, D.; Schadlich, M.; Schulz, A.; Subrova, J.; Mhatre,K.N.; et al. Cpxm2 as a novel candidate for cardiac hypertrophy and failure in hypertension. Hypertens. Res. 2021, 45, 292–307.[CrossRef] [PubMed]

122. Zeng, Y.; Du, X.; Yao, X.; Qiu, Y.; Jiang, W.; Shen, J.; Li, L.; Liu, X. Mechanism of cell death of endothelial cells regulated bymechanical forces. J. Biomech. 2021, 131, 110917. [CrossRef] [PubMed]

123. Ashton, K.J.; Tupicoff, A.; Williams-Pritchard, G.; Kiessling, C.J.; See Hoe, L.E.; Headrick, J.P.; Peart, J.N. Unique transcriptionalprofile of sustained ligand-activated preconditioning in pre- and post-ischemic myocardium. PLoS ONE 2013, 8, e72278. [CrossRef][PubMed]

124. Sekino, Y.; Oue, N.; Mukai, S.; Shigematsu, Y.; Goto, K.; Sakamoto, N.; Sentani, K.; Hayashi, T.; Teishima, J.; Matsubara, A.; et al.Protocadherin B9 promotes resistance to bicalutamide and is associated with the survival of prostate cancer patients. Prostate2019, 79, 234–242. [CrossRef]

125. Gabbert, L.; Dilling, C.; Meybohm, P.; Burek, M. Deletion of protocadherin gamma C3 induces phenotypic and functional changesin brain microvascular endothelial cells in vitro. Front. Pharmacol. 2020, 11, 590144. [CrossRef]

126. Wang, H.L.; Zhang, C.L.; Qiu, Y.M.; Chen, A.Q.; Li, Y.N.; Hu, B. Dysfunction of the blood-brain barrier in cerebral microbleeds:From bedside to bench. Aging Dis. 2021, 12, 1898–1919. [CrossRef]

127. Du, J.; Dong, Y.; Li, Y. Identification and prognostic value exploration of cyclophosphamide (cytoxan)-centered chemotherapyresponse-associated genes in breast cancer. DNA Cell Biol. 2021, 40, 1356–1368. [CrossRef]

128. Bloodgood, D.W.; Hardaway, J.A.; Stanhope, C.M.; Pati, D.; Pina, M.M.; Neira, S.; Desai, S.; Boyt, K.M.; Palmiter, R.D.; Kash,T.L. Kappa opioid receptor and dynorphin signaling in the central amygdala regulates alcohol intake. Mol. Psychiatry 2021, 26,2187–2199. [CrossRef]

129. Uchiyama, M.; Mizukami, S.; Arima, K.; Nishimura, T.; Tomita, Y.; Abe, Y.; Tanaka, N.; Honda, Y.; Goto, H.; Hasegawa, M.; et al.Association between serum 25-hydroxyvitamin D and physical performance measures in middle-aged and old Japanese men andwomen: The Unzen study. PLoS ONE 2021, 16, e0261639. [CrossRef]

130. Landrum, M.; Lee, J.; Riley, G.; Jang, W.; Rubinstein, W.; Church, D.; Maglott, D. ClinVar: Public archive of relationships amongsequence variation and human phenotype. Nucleic Acids Res. 2014, 42, D980–D985. [CrossRef]

131. Elavarasi, A.; Dash, D.; Tripathi, M.; Bhatia, R. Cerebellar ataxia and neuropathy as presenting features of hepatitis-B relatedcirrhosis and portal hypertension. BMJ Case Rep. 2017, 2017, bcr2017221912. [CrossRef]

132. Miki, Y.; Kidoguchi, Y.; Sato, M.; Taketomi, Y.; Taya, C.; Muramatsu, K.; Gelb, M.H.; Yamamoto, K.; Murakami, M. Dual roles ofgroup iid phospholipase A2 in inflammation and cancer. J. Biol. Chem. 2016, 291, 15588–155601. [CrossRef] [PubMed]

133. Karpinska-Mirecka, A.; Bartosinska, J.; Krasowska, D. The impact of hypertension, diabetes, lipid disorders, overweight/obesityand nicotine dependence on health-related quality of life and psoriasis severity in psoriatic patients receiving systemic conven-tional and biological treatment. Int. J. Environ. Res. Public Health 2021, 18, 13167. [CrossRef] [PubMed]

134. Htwe, Y.M.; Wang, H.; Belvitch, P.; Meliton, L.; Bandela, M.; Letsiou, E.; Dudek, S.M. Group V phospholipase A2 mediatesendothelial dysfunction and acute lung injury caused by methicillin-resistant Staphylococcus Aureus. Cells 2021, 10, 1731.[CrossRef] [PubMed]

Int. J. Mol. Sci. 2022, 23, 2835 24 of 28

135. Launay, J.M.; Herve, P.; Callebert, J.; Mallat, Z.; Collet, C.; Doly, S.; Belmer, A.; Diaz, S.L.; Hatia, S.; Cote, F.; et al. Serotonin5-HT2B receptors are required for bone-marrow contribution to pulmonary arterial hypertension. Blood 2012, 119, 1772–1780.[CrossRef] [PubMed]

136. Wu, C.; Su, J.; Wang, X.; Wang, J.; Xiao, K.; Li, Y.; Xiao, Q.; Ling, M.; Xiao, Y.; Qin, C.; et al. Overexpression of the phospholipaseA2 group V gene in glioma tumors is associated with poor patient prognosis. Cancer Manag. Res. 2019, 11, 3139–3152. [CrossRef]

137. Li, B.; Yang, H.; Shen, B.; Huang, J.; Qin, Z. Procollagen-lysine, 2-oxoglutarate 5-dioxygenase 1 increases cellular proliferation andcolony formation capacity in lung cancer via activation of E2F transcription factor 1. Oncol. Lett. 2021, 22, 851. [CrossRef]

138. Li, J.; Lam, A.S.M.; Yau, S.T.Y.; Yiu, K.K.L.; Tsoi, K.K.F. Antihypertensive treatments and risks of lung Cancer: A large population-based cohort study in Hong Kong. BMC Cancer 2021, 21, 1202. [CrossRef]

139. Tian, L.; Zhou, H.; Wang, G.; Wang, W.Y.; Li, Y.; Xue, X. The relationship between PLOD1 expression level and glioma prognosisinvestigated using public databases. PeerJ 2021, 9, e11422. [CrossRef]

140. Mehta, M.B.; Shewale, S.V.; Sequeira, R.N.; Millar, J.S.; Hand, N.J.; Rader, D.J. Hepatic protein phosphatase 1 regulatory subunit3B (Ppp1r3b) promotes hepatic glycogen synthesis and thereby regulates fasting energy homeostasis. J. Biol. Chem. 2017, 292,10444–10454. [CrossRef]

141. Oben, A.; Jauk, V.; Battarbee, A.; Longo, S.; Szychowski, J.; Tita, A.; Harper, L. Value of HbA1c in obese women with gestationaldiabetes. Am. J. Perinatol. 2021. [CrossRef] [PubMed]

142. Panossian, A.; Seo, E.J.; Efferth, T. Novel molecular mechanisms for the adaptogenic effects of herbal extracts on isolated braincells using systems biology. Phytomedicine 2018, 50, 257–284. [CrossRef] [PubMed]

143. Tang, Y.; Fung, E.; Xu, A.; Lan, H.Y. C-reactive protein and ageing. Clin. Exp. Pharmacol. Physiol. 2017, 44, 9–14. [CrossRef][PubMed]

144. Touraine, P.; Martini, J.F.; Zafrani, B.; Durand, J.C.; Labaille, F.; Malet, C.; Nicolas, A.; Trivin, C.; Postel-Vinay, M.C.; Kuttenn, F.;et al. Increased expression of prolactin receptor gene assessed by quantitative polymerase chain reaction in human breast tumorsversus normal breast tissues. J. Clin. Endocrinol. Metab. 1998, 83, 667–674. [CrossRef]

145. Zhou, W.; Wang, Y.; Gao, H.; Jia, Y.; Xu, Y.; Wan, X.; Zhang, Z.; Yu, H.; Yan, S. Identification of key genes involved in pancreaticductal adenocarcinoma with diabetes mellitus based on gene expression profiling analysis. Pathol. Oncol. Res. 2021, 27, 604730.[CrossRef]

146. Zhao, C.Y.; Hua, C.H.; Li, C.H.; Zheng, R.Z.; Li, X.Y. High PYGL expression predicts poor prognosis in human gliomas. Front.Neurol. 2021, 12, 652931. [CrossRef]

147. Xia, W.; Su, L.; Jiao, J. Cold-induced protein RBM3 orchestrates neurogenesis via modulating Yap mRNA stability in cold stress. J.Cell Biol. 2018, 217, 3464–3479. [CrossRef]

148. Takahashi, Y.; Hori, M.; Shimoji, K.; Miyajima, M.; Akiyama, O.; Arai, H.; Aoki, S. Changes in delta ADC reflect intracranialpressure changes in craniosynostosis. Acta Radiol. Open 2017, 6, 2058460117728535. [CrossRef]

149. Jogi, A.; Brennan, D.J.; Ryden, L.; Magnusson, K.; Ferno, M.; Stal, O.; Borgquist, S.; Uhlen, M.; Landberg, G.; Pahlman, S.; et al.Nuclear expression of the RNA-binding protein RBM3 is associated with an improved clinical outcome in breast cancer. Mod.Pathol. 2009, 22, 1564–1574. [CrossRef]

150. Sarang, Z.; Saghy, T.; Budai, Z.; Ujlaky-Nagy, L.; Bedekovics, J.; Beke, L.; Mehes, G.; Nagy, G.; Ruhl, R.; Moise, A.R.; et al. Retinolsaturase knock-out mice are characterized by impaired clearance of apoptotic cells and develop mild autoimmunity. Biomolecules2019, 9, 737. [CrossRef]

151. Martin Calderon, L.; Chaudhury, M.; Pope, J.E. Healthcare utilization and economic burden in systemic sclerosis: A systematicreview. Rheumatology 2021, keab847. [CrossRef] [PubMed]

152. Zhao, L.; Zhu, X.; Xia, M.; Li, J.; Guo, A.Y.; Zhu, Y.; Yang, X. Quercetin ameliorates gut microbiota dysbiosis that driveshypothalamic damage and hepatic lipogenesis in monosodium glutamate-induced abdominal obesity. Front. Nutr. 2021, 8, 671353.[CrossRef] [PubMed]

153. Li, R.; Luo, S.Y.; Zuo, Z.G.; Yu, Z.; Chen, W.N.; Ye, Y.X.; Xia, M. Association between serum uric acid to creatinine ratio andmetabolic syndrome based on community residents in Chashan town, Dongguan city. Zhonghua Yu Fang Yi Xue Za Zhi (Chin. J.Prev. Med.) 2021, 55, 1449–1455.

154. Mei, J.; Hu, K.; Peng, X.; Wang, H.; Liu, C. Decreased expression of SLC16A12 mRNA predicts poor prognosis of patients withclear cell renal cell carcinoma. Medicine 2019, 98, e16624. [CrossRef]

155. Liu, J.; Bao, J.; Zhang, W.; Li, Q.; Hou, J.; Wei, X.; Huang, Y. The potential of visceral adipose tissue in distinguishing clear cellrenal cell carcinoma from renal angiomyolipoma with minimal fat. Cancer Manag. Res. 2021, 13, 8907–8914. [CrossRef]

156. Zuercher, J.; Neidhardt, J.; Magyar, I.; Labs, S.; Moore, A.T.; Tanner, F.C.; Waseem, N.; Schorderet, D.F.; Munier, F.L.; Bhattacharya,S.; et al. Alterations of the 5’untranslated region of SLC16A12 lead to age-related cataract. Investig. Ophthalmol. Vis. Sci. 2010, 51,3354–3361. [CrossRef]

157. Ang, M.J.; Afshari, N.A. Cataract and systemic disease: A review. Clin. Exp. Ophthalmol. 2021, 49, 118–127. [CrossRef]158. Christensen, H.L.; Barbuskaite, D.; Rojek, A.; Malte, H.; Christensen, I.B.; Fuchtbauer, A.C.; Fuchtbauer, E.M.; Wang, T.; Praetorius,

J.; Damkier, H.H. The choroid plexus sodium-bicarbonate cotransporter NBCe2 regulates mouse cerebrospinal fluid pH. J. Physiol.2018, 596, 4709–4728. [CrossRef]

159. Betz, M.V.; Nemec, K.B.; Zisman, A.L. Plant-based diets in kidney disease: Nephrology professionals’ perspective. J. Ren. Nutr.2021. [CrossRef]

Int. J. Mol. Sci. 2022, 23, 2835 25 of 28

160. Kant, S.; Stopa, E.G.; Johanson, C.E.; Baird, A.; Silverberg, G.D. Choroid plexus genes for CSF production and brain homeostasisare altered in Alzheimer’s disease. Fluids Barriers CNS 2018, 15, 34. [CrossRef]

161. Coatl-Cuaya, H.; Tendilla-Beltran, H.; de Jesús-Vásquez, L.M.; Garces-Ramirez, L.; Gomez-Villalobos, M.J.; Flores, G. Losartanenhances cognitive and structural neuroplasticity impairments in spontaneously hypertensive rats. J. Chem. Neuroanat. 2021, 120,102061. [CrossRef] [PubMed]

162. Yuting, Y.; Lafeng, F.; Qiwei, H. Secreted modular calcium-binding protein 2 promotes high fat diet (HFD)-induced hepaticsteatosis through enhancing lipid deposition, fibrosis and inflammation via targeting TGF-β1. Biochem. Biophys. Res. Commun.2019, 509, 48–55. [CrossRef] [PubMed]

163. Calvopina, D.A.; Lewindon, P.J.; Ramm, L.E.; Noble, C.; Hartel, G.F.; Leung, D.H.; Ramm, G.A. Gamma-glutamyl transpeptidase-to-platelet ratio as a biomarker of liver disease and hepatic fibrosis severity in paediatric cystic fibrosis. J. Cyst. Fibros. 2021.[CrossRef] [PubMed]

164. Hyun, C.L.; Park, S.J.; Kim, H.S.; Song, H.J.; Kim, H.U.; Lee, C.; Lee, D.H.; Maeng, Y.H.; Kim, Y.S.; Jang, B. The intestinal stem cellmarker SMOC2 is an independent prognostic marker associated with better survival in gastric cancer. Anticancer Res. 2021, 41,3689–3698. [CrossRef] [PubMed]

165. Gomez-Abenza, E.; Ibanez-Molero, S.; Garcia-Moreno, D.; Fuentes, I.; Zon, L.I.; Mione, M.C.; Cayuela, M.L.; Gabellini, C.; Mulero,V. Zebrafish modeling reveals that SPINT1 regulates the aggressiveness of skin cutaneous melanoma and its crosstalk with tumorimmune microenvironment. J. Exp. Clin. Cancer Res. 2019, 38, 405. [CrossRef]

166. Soffietti, R.; Ruda, R.; Mutani, R. Management of brain metastases. J. Neurol. 2002, 249, 1357–1369. [CrossRef]167. Huang, H.P.; Chang, M.H.; Chen, Y.T.; Hsu, H.Y.; Chiang, C.L.; Cheng, T.S.; Wu, Y.M.; Wu, M.Z.; Hsu, Y.C.; Shen, C.C.; et al.

Persistent elevation of hepatocyte growth factor activator inhibitors in cholangiopathies affects liver fibrosis and differentiation.Hepatology 2012, 55, 161–172. [CrossRef]

168. Takashima, Y.; Keino-Masu, K.; Yashiro, H.; Hara, S.; Suzuki, T.; van Kuppevelt, T.H.; Masu, M.; Nagata, M. Heparan sulfate6-O-endosulfatases, Sulf1 and Sulf2, regulate glomerular integrity by modulating growth factor signaling. Am. J. Physiol. RenalPhysiol. 2016, 310, F395–F408. [CrossRef]

169. Saito, N.; Toyoda, M.; Ono, M.; Kondo, M.; Moriya, H.; Kimura, M.; Sawada, K.; Fukagawa, M. Regulation of blood pressure andphosphorylation of β1-integrin in renal tissue in a rat model of diabetic nephropathy. Tokai J. Exp. Clin. Med. 2021, 46, 172–179.

170. Lee, H.Y.; Yeh, B.W.; Chan, T.C.; Yang, K.F.; Li, W.M.; Huang, C.N.; Ke, H.L.; Li, C.C.; Yeh, H.C.; Liang, P.I.; et al. Sulfatase-1overexpression indicates poor prognosis in urothelial carcinoma of the urinary bladder and upper tract. Oncotarget 2017, 8,47216–47229. [CrossRef]

171. Chen, J.S.; Lu, C.L.; Huang, L.C.; Shen, C.H.; Chen, S.C. Chronic kidney disease is associated with upper tract urothelial carcinoma:A nationwide population-based cohort study in Taiwan. Medicine 2016, 95, e3255. [CrossRef] [PubMed]

172. Clarke, W.T.; Edwards, B.; McCullagh, K.J.; Kemp, M.W.; Moorwood, C.; Sherman, D.L.; Burgess, M.; Davies, K.E. Syncoilinmodulates peripherin filament networks and is necessary for large-calibre motor neurons. J. Cell Sci. 2010, 123, 2543–2552.[CrossRef] [PubMed]

173. Silva, P.S.C.D.; Boing, A.F. Factors associated with leisure-time physical activity: Analysis of Brazilians with chronic diseases.Cien Saude Colet (Sci. Collect. Health) 2021, 26, 5727–5738. [CrossRef] [PubMed]

174. Wang, D.; Deng, L.; Xu, X.; Ji, Y.; Jiao, Z. Elevated SYNC expression is associated with gastric tumorigenesis and infiltrationof M2-polarized macrophages in the gastric tumor immune microenvironment. Genet. Test. Mol. Biomarkers 2021, 25, 236–246.[CrossRef]

175. Hao, X.L.; Gao, L.Y.; Deng, X.J.; Han, F.; Chen, H.Q.; Jiang, X.; Liu, W.B.; Wang, D.D.; Chen, J.P.; Cui, Z.H.; et al. Identification ofTC2N as a novel promising suppressor of PI3K-AKT signaling in breast cancer. Cell Death Dis. 2019, 10, 424. [CrossRef]

176. Xu, J.; Ou, X.; Li, J.; Cai, Q.; Sun, K.; Ye, J.; Peng, J. Overexpression of TC2N is associated with poor prognosis in gastric cancer. J.Cancer. 2021, 12, 807–817. [CrossRef]

177. Jeng, J.Y.; Harasztosi, C.; Carlton, A.J.; Corns, L.F.; Marchetta, P.; Johnson, S.L.; Goodyear, R.J.; Legan, K.P.; Ruttiger, L.; Richardson,G.P.; et al. MET currents and otoacoustic emissions from mice with a detached tectorial membrane indicate the extracellularmatrix regulates Ca2+ near stereocilia. J. Physiol. 2021, 599, 2015–2036. [CrossRef]

178. Frisina, R.D.; Wheeler, H.E.; Fossa, S.D.; Kerns, S.L.; Fung, C.; Sesso, H.D.; Monahan, P.O.; Feldman, D.R.; Hamilton, R.; Vaughn,D.J.; et al. Comprehensive audiometric analysis of hearing impairment and tinnitus after cisplatin-based chemotherapy insurvivors of adult-onset cancer. J. Clin. Oncol. 2016, 34, 2712–2720. [CrossRef]

179. Solares, C.A.; Edling, A.E.; Johnson, J.M.; Baek, M.J.; Hirose, K.; Hughes, G.B.; Tuohy, V.K. Murine autoimmune hearing lossmediated by CD4+ T cells specific for inner ear peptides. J. Clin. Investig. 2004, 113, 1210–1217. [CrossRef]

180. Xu, Z.; Ruan, J.; Pan, L.; Chen, C. Candidate genes identified in systemic sclerosis-related pulmonary arterial hypertension wereassociated with immunity, inflammation, and cytokines. Cardiovasc. Ther. 2021, 2021, 6651009. [CrossRef]

181. Kiermayer, C.; Northrup, E.; Schrewe, A.; Walch, A.; de Angelis, M.H.; Schoensiegel, F.; Zischka, H.; Prehn, C.; Adamski, J.;Bekeredjian, R.; et al. Heart-specific knockout of the mitochondrial thioredoxin reductase (Txnrd2) induces metabolic andcontractile dysfunction in the aging myocardium. J. Am. Heart Assoc. 2015, 4, e002153. [CrossRef] [PubMed]

182. Liang, J.; Zhu, R.; Yang, Y.; Li, R.; Hong, C.; Luo, C. A predictive model for dilated cardiomyopathy with pulmonary hypertension.ESC Heart Fail. 2021, 8, 4255–4264. [CrossRef] [PubMed]

Int. J. Mol. Sci. 2022, 23, 2835 26 of 28

183. Mou, D.; Ding, D.; Yan, H.; Qin, B.; Dong, Y.; Li, Z.; Che, L.; Fang, Z.; Xu, S.; Lin, Y.; et al. Maternal supplementation of organicselenium during gestation improves sows and offspring antioxidant capacity and inflammatory status and promotes embryosurvival. Food Funct. 2020, 11, 7748–7761. [CrossRef] [PubMed]

184. Trinchese, G.; Cimmino, F.; Cavaliere, G.; Rosati, L.; Catapano, A.; Sorriento, D.; Murru, E.; Bernardo, L.; Pagani, L.; Bergamo, P.;et al. Heart mitochondrial metabolic flexibility and redox status are improved by donkey and human milk intake. Antioxidants2021, 10, 1807. [CrossRef] [PubMed]

185. Busceti, C.L.; Cotugno, M.; Bianchi, F.; Forte, M.; Stanzione, R.; Marchitti, S.; Battaglia, G.; Nicoletti, F.; Fornai, F.; Rubattu, S.Brain overexpression of uncoupling protein-2 (UCP2) delays renal damage and stroke occurrence in stroke-prone spontaneouslyhypertensive rats. Int. J. Mol. Sci. 2020, 21, 4289. [CrossRef] [PubMed]

186. Kim, J.W.; Cardin, D.B.; Vaishampayan, U.N.; Kato, S.; Grossman, S.R.; Glazer, P.M.; Shyr, Y.; Ivy, S.P.; LoRusso, P.M. Clinicalactivity and safety of cediranib and olaparib combination in patients with metastatic pancreatic ductal adenocarcinoma withoutbrca mutation. Oncologist 2021, 26, e1104–e1109. [CrossRef]

187. Albert, F.W.; Somel, M.; Carneiro, M.; Aximu-Petri, A.; Halbwax, M.; Thalmann, O.; Blanco-Aguiar, J.A.; Plyusnina, I.Z.; Trut,L.; Villafuerte, R.; et al. A comparison of brain gene expression levels in domesticated and wild animals. PLoS Genet. 2012, 8,e1002962. [CrossRef]

188. Sato, D.X.; Rafati, N.; Ring, H.; Younis, S.; Feng, C.; Blanco-Aguiar, J.A.; Rubin, C.J.; Villafuerte, R.; Hallbook, F.; Carneiro, M.; et al.Brain transcriptomics of wild and domestic rabbits suggests that changes in dopamine signaling and ciliary function contributedto evolution of tameness. Genome Biol. Evol. 2020, 12, 1918–1928. [CrossRef]

189. Yang, X.; Zhang, H.; Shang, J.; Liu, G.; Xia, T.; Zhao, C.; Sun, G.; Dou, H. Comparative analysis of the blood transcriptomesbetween wolves and dogs. Anim. Genet. 2018, 49, 291–302. [CrossRef]

190. Hekman, J.P.; Johnson, J.L.; Edwards, W.; Vladimirova, A.V.; Gulevich, R.G.; Ford, A.L.; Kharlamova, A.V.; Herbeck, Y.; Acland,G.M.; Raetzman, L.T.; et al. Anterior pituitary transcriptome suggests differences in ACTH release in tame and aggressive foxes.G3 2018, 8, 859–873. [CrossRef]

191. Long, K.; Mao, K.; Che, T.; Zhang, J.; Qiu, W.; Wang, Y.; Tang, Q.; Ma, J.; Li, M.; Li, X. Transcriptome differences in frontal cortexbetween wild boar and domesticated pig. Anim. Sci. J. 2018, 89, 848–857. [CrossRef] [PubMed]

192. Yang, Y.; Adeola, A.C.; Xie, H.B.; Zhang, Y.P. Genomic and transcriptomic analyses reveal selection of genes for puberty in BamaXiang pigs. Zool. Res. 2018, 39, 424–430. [PubMed]

193. Fallahshahroudi, A.; Lotvedt, P.; Belteky, J.; Altimiras, J.; Jensen, P. Changes in pituitary gene expression may underlie multipledomesticated traits in chickens. Heredity 2019, 122, 195–204. [CrossRef]

194. Samet, H. A top-down quadtree traversal algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 1985, 7, 94–98. [CrossRef] [PubMed]195. Sun, G.-L.; Shen, W.; Wen, J.-F. Triosephosphate Isomerase Genes in Two Trophic Modes of Euglenoids (Euglenophyceae) and

Their Phylogenetic Analysis. J. Eukaryot. Microbiol. 2008, 55, 170–177. [CrossRef] [PubMed]196. Morozova, O.V.; Alekseeva, A.E.; Sashina, T.A.; Brusnigina, N.F.; Epifanova, N.V.; Kashnikov, A.U.; Zverev, V.V.; Novikova, N.A.

Phylodynamics of G4P [8] and G2P [4] strains of rotavirus A isolated in Russia in 2017 based on full-genome analyses. VirusGenes 2020, 56, 537–545. [CrossRef] [PubMed]

197. Hakizimana, J.N.; Yona, C.; Kamana, O.; Nauwynck, H.; Misinzo, G. African Swine Fever Virus Circulation between Tanzaniaand Neighboring Countries: A Systematic Review and Meta-Analysis. Viruses 2021, 13, 306. [CrossRef] [PubMed]

198. Zhang, Y.; Katoh, T.K.; Finet, C.; Izumitani, H.F.; Toda, M.J.; Watabe, H.-A.; Katoh, T. Phylogeny and evolution of mycophagy inthe Zygothrica genus group (Diptera: Drosophilidae). Mol. Phylogenetics Evol. 2021, 163, 107257. [CrossRef]

199. Crowley, J.P.; Metzger, J.B.; Merrill, E.W.; Valeri, C.R. Whole blood viscosity in beta thalassemia minor. Ann. Clin. Lab. Sci. 1992,22, 229–235.

200. Philibert, C.; Bouillot, S.; Huber, P.; Faury, G. Protocadherin-12 deficiency leads to modifications in the structure and function ofarteries in mice. Pathol. Biol. 2012, 60, 34–40. [CrossRef]

201. Mukai, S.; Oue, N.; Oshima, T.; Imai, T.; Sekino, Y.; Honma, R.; Sakamoto, N.; Sentani, K.; Kuniyasu, H.; Egi, H.; et al.Overexpression of PCDHB9 promotes peritoneal metastasis and correlates with poor prognosis in patients with gastric cancer. J.Pathol. 2017, 243, 100–110. [CrossRef] [PubMed]

202. Ponomarenko, M.; Rasskazov, D.; Chadaeva, I.; Sharypova, E.; Drachkova, I.; Oshchepkov, D.; Ponomarenko, P.; Savinkova, L.;Oshchepkova, E.; Nazarenko, M.; et al. Candidate SNP markers of atherogenesis significantly shifting the affinity of TATA-bindingprotein for human gene promoters show stabilizing natural selection as a sum of neutral drift accelerating atherogenesis anddirectional natural selection slowing it. Int. J. Mol. Sci. 2020, 21, 1045. [CrossRef] [PubMed]

203. Ponomarenko, M.; Rasskazov, D.; Arkova, O.; Ponomarenko, P.; Suslov, V.; Savinkova, L.; Kolchanov, N. How to useSNP_TATA_Comparator to find a significant change in gene expression caused by the regulatory SNP of this gene’s promoter viaa change in affinity of the TATA-binding protein for this promoter. Biomed. Res. Int. 2015, 2015, 359835. [CrossRef] [PubMed]

204. Varzari, A.; Tudor, E.; Bodrug, N.; Corloteanu, A.; Axentii, E.; Deyneko, I.V. Age-specific association of CCL5 gene polymorphismwith pulmonary tuberculosis: A case-control study. Genet. Test. Mol. Biomark. 2018, 22, 281–287. [CrossRef] [PubMed]

205. Cho, S.N.; Choi, J.A.; Lee, J.; Son, S.H.; Lee, S.A.; Nguyen, T.D.; Choi, S.Y.; Song, C.H. Ang II-Induced hypertension exacerbatesthe pathogenesis of tuberculosis. Cells 2021, 10, 2478. [CrossRef] [PubMed]

206. Haeussler, M.; Raney, B.; Hinrichs, A.; Clawson, H.; Zweig, A.; Karolchik, D.; Casper, J.; Speir, M.; Haussler, D.; Kent, W.Navigating protected genomics data with UCSC Genome Browser in a box. Bioinformatics 2015, 31, 764–766. [CrossRef] [PubMed]

Int. J. Mol. Sci. 2022, 23, 2835 27 of 28

207. Stajich, J.E.; Block, D.; Boulez, K.; Brenner, S.E.; Chervitz, S.A.; Dagdigian, C.; Fuellen, G.; Gilbert, J.G.; Korf, I.; Lapp, H.; et al.The Bioperl toolkit: Perl modules for the life sciences. Genome Res. 2002, 12, 1611–1618. [CrossRef]

208. Waardenberg, A.; Basset, S.; Bouveret, R.; Harvey, R. CompGO: An R package, for comparing and visualizing Gene Ontologyenrichment differences between DNA binding experiments. BMC Bioinform. 2015, 16, 275. [CrossRef]

209. Zerbino, D.; Wilder, S.; Johnson, N.; Juettemann, T.; Flicek, P. The Ensembl regulatory build. Genome Biol. 2015, 16, 56. [CrossRef]210. Day, I.N. dbSNP in the detail and copy number complexities. Hum. Mutat. 2010, 31, 2–4. [CrossRef]211. Martiney, J.A.; Cerami, A.; Slater, A.F. Inhibition of hemozoin formation in Plasmodium falciparum trophozoite extracts by heme

analogs: Possible implication in the resistance to malaria conferred by the beta-thalassemia trait. Mol. Med. 1996, 2, 236–246.[CrossRef] [PubMed]

212. Ponomarenko, P.; Savinkova, L.; Drachkova, I.; Lysova, M.; Arshinova, T.; Ponomarenko, M.; Kolchanov, N. A step-by-step modelof TBP/TATA box binding allows predicting human hereditary diseases by single nucleotide polymorphism. Dokl. Biochem.Biophys. 2008, 419, 88–92. [CrossRef] [PubMed]

213. Delgadillo, R.; Whittington, J.; Parkhurst, L.; Parkhurst, L. The TBP core domain in solution variably bends TATA sequences via athree-step binding mechanism. Biochemistry 2009, 48, 1801–1809. [CrossRef] [PubMed]

214. Hahn, S.; Buratowski, S.; Sharp, P.; Guarente, L. Yeast TATA-binding protein TFIID binds to TATA elements with both consensusand nonconsensus DNA sequences. Proc. Natl. Acad. Sci. USA 1989, 86, 5718–5722. [CrossRef]

215. Karas, H.; Knuppel, R.; Schulz, W.; Sklenar, H.; Wingender, E. Combining structural analysis of DNA with search routines for thedetection of transcription regulatory elements. Comput. Applic. Biosci. 1996, 12, 441–446. [CrossRef]

216. Ponomarenko, M.P.; Ponomarenko, J.V.; Frolov, A.S.; Podkolodny, N.L.; Savinkova, L.K.; Kolchanov, N.A.; Overton, G.C.Identification of sequence-dependent features correlating to activity of DNA sites interacting with proteins. Bioinformatics 1999,15, 687–703. [CrossRef]

217. Bucher, P. Weight matrix descriptions of four eukaryotic RNA polymerase II promoter elements derived from 502 unrelatedpromoter sequences. J. Mol. Biol. 1990, 212, 563–578. [CrossRef]

218. Flatters, D.; Lavery, R. Sequence-dependent dynamics of TATA-Box binding sites. Biophys. J. 1998, 75, 372–381. [CrossRef]219. Ponomarenko, P.M.; Suslov, V.V.; Savinkova, L.K.; Ponomarenko, M.P.; Kolchanov, N.A. A precise equilibrium equation for four

steps of binding between TBP and TATA-box allows for the prediction of phenotypical expression upon mutation. Biophysics2010, 55, 358–369. [CrossRef]

220. Mogno, I.; Vallania, F.; Mitra, R.D.; and Cohen, B.A. TATA is a modular component of synthetic promoters. Genome Res. 2010, 20,1391–1397. [CrossRef]

221. Pugh, B. Purification of the human TATA-binding protein, TBP. Methods Mol. Biol. 1995, 37, 359–367.222. Savinkova, L.; Drachkova, I.; Arshinova, T.; Ponomarenko, P.; Ponomarenko, M.; Kolchanov, N. An experimental verification of

the predicted effects of promoter TATA-box polymorphisms associated with human diseases on interactions between the TATAboxes and TATA-binding protein. PLoS ONE 2013, 8, e54626.

223. Drachkova, I.; Savinkova, L.; Arshinova, T.; Ponomarenko, M.; Peltek, S.; Kolchanov, N. The mechanism by which TATA-boxpolymorphisms associated with human hereditary diseases influence interactions with the TATA-binding protein. Hum. Mutat.2014, 35, 601–608. [CrossRef] [PubMed]

224. Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotidesequences. J. Mol. Evol. 1980, 16, 111–120. [CrossRef]

225. Li, W.H.; Wu, C.I.; Luo, C.C. A new method for estimating synonymous and nonsynonymous rates of nucleotide substitutionconsidering the relative likelihood of nucleotide and codon changes. Mol. Biol. Evol. 1985, 2, 150–174. [PubMed]

226. Oggenfuss, U.; Badet, T.; Wicker, T.; Hartmann, F.E.; Singh, N.K.; Abraham, L.; Karisto, P.; Vonlanthen, T.; Mundt, C.; McDonald,B.A.; et al. A population-level invasion by transposable elements triggers genome expansion in a fungal pathogen. eLife 2021, 10,e69249. [CrossRef] [PubMed]

227. Kasowski, M.; Grubert, F.; Heffelfinger, C.; Hariharan, M.; Asabere, A.; Waszak, S.; Habegger, L.; Rozowsky, J.; Shi, M.; Urban, A.;et al. Variation in transcription factor binding among humans. Science 2010, 328, 232–235. [CrossRef]

228. 1000 Genomes Project Consortium; Abecasis, G.; Auton, A.; Brooks, L.; DePristo, M.; Durbin, R.; Handsaker, R.; Kang, H.; Marth,G.; McVean, G.; et al. An integrated map of genetic variation from 1.092 human genomes. Nature 2012, 491, 56–65.

229. Haldane, J.B.S. The cost of natural selection. J. Genet. 1957, 55, 511–524. [CrossRef]230. Kimura, M. Evolutionary rate at the molecular level. Nature 1968, 217, 624–626. [CrossRef]231. Hill, A.E.; Plyler, Z.E.; Tiwari, H.; Patki, A.; Tully, J.P.; McAtee, C.W.; Moseley, L.A.; Sorscher, E.J. Longevity and plasticity of CFTR

provide an argument for noncanonical SNP organization in hominid DNA. PLoS ONE 2014, 9, e109186. [CrossRef] [PubMed]232. Naumenko, E.; Popova, N.; Nikulina, E.; Dygalo, N.; Shishkina, G.; Borodin, P.; Markel, A. Behavior, adrenocortical activity, and

brain monoamines in Norway rats selected for reduced aggressiveness towards man. Pharmacol. Biochem. Behav. 1989, 33, 85–91.[CrossRef]

233. Paxinos, G.; Watson, C.R. The Rat Brain in Stereotaxic Coordinates, 7th ed.; Academic Press: London, UK, 2013; p. 472.234. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120.

[CrossRef] [PubMed]235. Kim, D.; Pertea, G.; Trapnell, C.; Pimentel, H.; Kelley, R.; Salzberg, S.L. TopHat2: Accurate alignment of transcriptomes in the

presence of insertions, deletions and gene fusions. Genome Biol. 2013, 14, 36. [CrossRef]

Int. J. Mol. Sci. 2022, 23, 2835 28 of 28

236. Li, H.; Handsaker, B.; Wysoker, A.; Fennell, T.; Ruan, J.; Homer, N.; Marth, G.; Abecasis, G.; Durbin, R. The Sequence align-ment/map (SAM) format and SAMtools. Bioinformatics 2009, 25, 2078–2079. [CrossRef]

237. Anders, S.; Pyl, P.T.; Huber, W. HTSeq—A Python framework to work with high-throughput sequencing data. Bioinformatics 2015,31, 166–169. [CrossRef]

238. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. GenomeBiol. 2014, 15, 550. [CrossRef]

239. Anders, S.; Huber, W. Differential expression analysis for sequence count data. Genome Biol. 2010, 11, 106. [CrossRef]240. Ye, J.; Coulouris, G.; Zaretskaya, I.; Cutcutache, I.; Rozen, S.; Madden, T.L. Primer-BLAST: A tool to design target-specific primers

for polymerase chain reaction. BMC Bioinform. 2012, 13, 134. [CrossRef]241. Tian, L.; Chen, Y.; Wang, D.W.; Liu, X.H. Validation of reference genes via qRT-PCR in multiple conditions in brandt’s voles,

lasiopodomys brandtii. Animals 2021, 11, 897. [CrossRef]242. Zamani, A.; Powell, K.L.; May, A.; Semple, B.D. Validation of reference genes for gene expression analysis following experimental

traumatic brain injury in a pediatric mouse model. Brain Res. Bull. 2020, 156, 43–49. [CrossRef] [PubMed]243. Gholami, K.; Loh, S.Y.; Salleh, N.; Lam, S.K.; Hoe, S.Z. Selection of suitable endogenous reference genes for qPCR in kidney and

hypothalamus of rats under testosterone influence. PLoS ONE 2017, 12, e0176368. [CrossRef] [PubMed]244. Penning, L.C.; Vrieling, H.E.; Brinkhof, B.; Riemers, F.M.; Rothuizen, J.; Rutteman, G.R.; Hazewinkel, H.A. A validation of 10

feline reference genes for gene expression measurements in snap-frozen tissues. Vet. Immunol. Immunopathol. 2007, 120, 212–222.[CrossRef] [PubMed]